diff --git a/Data/CU-DSS/CU-DSS-Ahmed-El-Alaoui-.md b/Data/CU-DSS/CU-DSS-Ahmed-El-Alaoui-.md new file mode 100644 index 0000000..dd35cc4 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Ahmed-El-Alaoui-.md @@ -0,0 +1,158 @@ +--- +bio-current: + name-cn: + name_en: Ahmed El Alaoui + email: +- ae333@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: +- Department of Statistics and Data Science[https://stat.cornell.edu/] + major: #### + title-raw: Assistant Professor + title: Assistant Professor + interests: +- High-dimensional phenomena in statistics and probability theory +- Related algorithmic questions +- Statistical physics + homepage: +- https://elalaoui.stat.cornell.edu/ + github: + googlescholar: + aminer: + status: 在职 + last-update: +edu-phd: + university: UC Berkeley + school: +- Department of Statistics[https://statistics.berkeley.edu/] + email: + date-start: 2013 + date-end: 2018 + advisor: +- Andrea Montanari[montanari@stanford.edu] + degree: phd +edu-master: + university: Ecole Normale Supérieure/Ecole des Ponts Paristech + school: + date-start: 2012 + date-end: 2013 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Ecole Polytechnique + school: +- Institut Ploytechnique[https://www.polytechnique.edu/en] + major: Applied math + date-start: 2009 + date-end: 2012 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: +- https://arxiv.org/search/math?searchtype=author&query=Alaoui%2C+A+E #arxiv + research: + software: + project: + blog: + arxiv: +- https://arxiv.org/search/math?searchtype=author&query=Alaoui%2C+A+E + linkedin: +- https://www.linkedin.com/in/elalaouiahmed/ + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: Stanford University + school: + - Department of Statistics[https://statistics.stanford.edu/] + email: + date-start: 2018 + date-end: 2020 + advisor: + - Andrea Montanari[montanari@stanford.edu] +--- + +# Profile + +![Ahmed El Alaoui ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/El%20Alaoui%20Ahmed_0.jpg?itok=hopYkTeu) + +# Biography[English] +High-dimensional phenomena in statistics and probability theory. Related algorithmic questions. Statistical physics. + +The questions that drive my research are the fundamental limits of extracting information from noisy data, and the algorithmic feasibility of this task. I like to think about large random structures such as matrices, graphs and tensors, and understand how sudden changes in their structural properties have statistical and algorithmic consequences. + +# Biography[中文] + +# Interests[English] +- High-dimensional phenomena in statistics and probability theory +- Related algorithmic questions +- Statistical physics + +# Interests[中文] + +# Education[English] +- (2018-2020) Postdoctoral researcher, Stanford University. Hosted by Andrea Montanari. +- (2013-2018) Ph.D. Electrical Engineering and Computer Sciences, UC Berkeley. Advised by Michael I. Jordan. +- (2012-2013) M.Sc. Mathématiques, Vision et Apprentissage, Ecole Normale Supérieure/Ecole des Ponts Paristech. +- (2009-2012) Eng.Deg. Applied math, Ecole Polytechnique. + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- (2021-) Assistant professor, Department of Statistics and Data Science, Cornell University. +- (Fall 2020) Richard M. Karp Research Fellow, Simons Institute for the Theory of Computing, UC Berkeley. +- (2013.9-2020.7) Postdoctoral Researcher, Stanford University. +- (2013.8-2018.8) PhD candidate, UC Berkeley. +- (2013.3-2013.7) Research assistant, Center of Learing and Visual Computing. + +# Work experience[中文] + +# Publication[English] +- **An Information-theoretic view of Stochastic Localization**. **A. El Alaoui**, A. Montanari. Preprint 2021. [arxiv]. +- **Efficient Z_2 synchronization on Z^d under symmetry-preserving side information**. Preprint 2021. [arxiv]. +- **Algorithmic pure states for the negative spherical perceptron**. **A. El Alaoui**, M. Sellke. Preprint 2020. [arxiv]. +- **Algorithmic thresholds in mean-field spin glasses**. **A. El Alaoui**, A. Montanari. Preprint 2020. [arxiv]. +- **Imputation for high-dimensional linear regression**. K. Chandrasekher, **A. El Alaoui**, A. Montanari. Preprint 2020. [arxiv]. +- **Finite-size corrections and likelihood ratio fluctuations in the spiked Wigner model**. **A. El Alaoui**, F. Krzakala, M. I. Jordan. Preprint 2017. [arxiv]. (Working paper; will be substantially revised.) +- **On the computational tractability of statistical estimation on amenable graphs**. **A. El Alaoui**, A. Montanari. Probability Theory and Related Fields (to appear 2021+). [arxiv]. +- **Optimization of mean-field spin glasses**. **A. El Alaoui**, A. Montanari, M. Sellke. Annals of Probability (to appear 2021+). [arxiv]. +- **Fundamental limits of detection in the spiked Wigner model**. **A. El Alaoui**, F. Krzakala, M. I. Jordan. Annals of Statistics, Vol 48, No. 2, 863-885, 2020. [journal, arxiv]. +- **Decoding from pooled data: Sharp information-theoretic bounds**. **A. El Alaoui**, A. Ramdas, F. Krzakala, L. Zdeborová, M. I. Jordan. SIAM Journal on Mathematics of Data Science 1-1 (2019), pp. 161-188. [journal, arxiv]. +- **Decoding from pooled data: Phase transitions of message passing**. **A. El Alaoui**, A. Ramdas, F. Krzakala, L. Zdeborová, M. I. Jordan. IEEE Transactions on Information Theory, 65, 572-585, 2019. [journal, arxiv]. +- **Presented at IEEE International Symposium on Information Theory** (ISIT) 2017.[proc.]. +- **The Kikuchi hierarchy and tensor PCA. A. Wein**, **A. El Alaoui**, C. Moore. 60th Annual Conference on Foundations of Computer Science (FOCS) 2019. [arxiv]. +- **Detection limits in the high-dimensional spiked rectangular model**. **A. El Alaoui**, M. I. Jordan. 31th Annual Conference on Learning Theory (COLT), PMLR 75:410-438, 2018. [proc., arxiv]. +- **Tight query complexity lower bounds for PCA via finite sample deformed Wigner law**. M. Simchowitz, **A. El Alaoui**, B. Recht. 50th Annual Symposium on the Theory of Computing (STOC) 2018. [proc., arxiv]. Here’s an earlier version (not intended for publication) with slightly suboptimal results. +- **Estimation in the spiked Wigner model: A short proof of the replica formula**. **A. El Alaoui**, F. Krzakala. IEEE International Symposium on Information Theory (ISIT) 2018. [proc., arxiv]. +- **Asymptotic behavior of Lp-based Laplacian regularization in semi-supervised learning**. **A. El Alaoui**, X. Cheng, A. Ramdas, M. J. Wainwright, M. I. Jordan. 29th Annual Conference on Learning Theory (COLT), PMLR 49:879-906, 2016. [proc., arxiv]. +- **Fast randomized kernel ridge regression with statistical guarantees**. **A. El Alaoui**, M. W. Mahoney. Advances in Neural Information Processing Systems (NIPS) 28, 2015. [proc., arxiv]. +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Amy-Kuceyeski-.md b/Data/CU-DSS/CU-DSS-Amy-Kuceyeski-.md new file mode 100644 index 0000000..d05af24 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Amy-Kuceyeski-.md @@ -0,0 +1,184 @@ +--- +bio-current: + name-cn: + name_en: Amy Kuceyeski + email: + - amk2012@med.cornell.edu + sex: female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Mathematics in Radiology + title-raw: Associate Professor + title: Associate Professor + interests: + - using machine learning techniques applied to neuroimaging metrics to better understand + - diagnose and treat neurological disorders + homepage: + - http://vivo.med.cornell.edu/display/cwid-amk2012 + github: # + googlescholar: + - https://scholar.google.com/citations?hl=en&user=cfUvMIYAAAAJ + aminer: + - https://www.aminer.cn/profile/amy-kuceyeski/53f47301dabfaec09f270729 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Case Western Reserve University + school: + - Depatment of Mathematics[https://mathstats.case.edu/] + email: + date-start: + date-end: 2009 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科BS + university: Mount Union College + school: + - Deaptment of Mathematics and Computer Science[https://www.mountunion.edu/dept-math-computer-science] + major: Mathematics + date-start: + date-end: 2004 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/search/cs?searchtype=author&query=Kuceyeski%2C+A + linkedin: + - https://www.linkedin.com/in/amy-kuceyeski-7314826/ + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Amy Kuceyeski ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Amy_Kuceyeski.jpeg?itok=cJckdeGx) + +# Biography[English] +Amy Kuceyeski is an adjunct associate professor in the Department of Statistics and Data Science and an associate professor of mathematics in the Radiology Department and Brain and Mind Research Institute at Weill Cornell Medicine. She performs research in quantitative neuroimaging of neurological disorders, including multiple sclerosis, stroke, traumatic brain injury and disorders of consciousness. + + +# Biography[中文] + +# Interests[English] +- using machine learning techniques applied to neuroimaging metrics to better understand +- diagnose and treat neurological disorders + +# Interests[中文] + +# Education[English] +- Case Western Reserve University, PhD, Applied Mathematics, May 2009 +- Mount Union College, BS, Mathematics, May 2004, Graduated Summa Cum Laude + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Associate Professor of Mathematics in Neuroscience, Brain and Mind Research Institute , Weill Cornell Medical College 2019 - +- Associate Professor of Mathematics in Radiology, Radiology , Weill Cornell Medical College 2019 - +- Assistant Professor of Mathematics in Neuroscience, Brain and Mind Research Institute , Weill Cornell Medical College 2015 - 2019 +- Assistant Professor of Mathematics in Radiology, Radiology , Weill Cornell Medical College 2015 - 2019 +- Instructor of Mathematics in Neuroscience, Brain and Mind Research Institute , Weill Cornell Medical College 2013 - 2015 +- Instructor of Mathematics in Radiology, Radiology , Weill Cornell Medical College 2013 - 2015 +- Postdoctoral Associate in Radiology, Radiology , Weill Cornell Medical College 2009 - 2013 + +# Work experience[中文] + +# Publication[English] +- **Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. NeuroImage**. Clinical. 2021 Academic Article +- **Machine learning to investigate superficial white matter integrity in early multiple sclerosis. Journal of neuroimaging : official journal of the American Society of Neuroimaging**. 2021 Academic Article +- **Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain and behavior**. 2021 Academic Article +- **Lesion features on magnetic resonance imaging discriminate multiple sclerosis patients. European journal of neurology**. 2021 Academic Article +- **Heritability and interindividual variability of regional structure-function coupling. Nature communications**. 2021 Academic Article +- **Estimated Regional White Matter Hyperintensity Burden, Resting State Functional Connectivity, and Cognitive Functions in Older Adults**. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry. 2021 Academic Article +- **Cortical response to naturalistic stimuli is largely predictable with deep neural networks**. Science advances. 2021 Academic Article +- **Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults**. Human brain mapping. 2021 Academic Article +- **Neuroimaging correlates of emotional response-inhibition discriminate between young depressed adults with and without sub-threshold bipolar symptoms (Emotional Response-inhibition in Young Depressed Adults).** Journal of affective disorders. 2020 Academic Article +- **A Multi-Ligand Imaging Study Exploring GABAergic Receptor Expression and Inflammation in Multiple Sclerosis**. Molecular imaging and biology. 2020 Academic Article +- **Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition**. Current opinion in neurology. 2020 Information Resource +- **Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging**. NeuroImage. 2020 Academic Article +- **Sex classification using long-range temporal dependence of resting-state functional MRI time series**. Human brain mapping. 2020 Academic Article +- **Functional Connectivity and Structural Disruption in the Default-Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis**. Journal of neuroimaging : official journal of the American Society of Neuroimaging. 2020 Academic Article +- **The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke**. Human brain mapping. 2020 Information Resource +- **Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke**. Neurorehabilitation and neural repair. 2020 Academic Article +- **Machine learning in resting-state fMRI analysis**. Magnetic resonance imaging. 2020 Information Resource +- **Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI**. Human brain mapping. 2019 Academic Article +- **Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction**. NeuroImage. 2019 Academic Article +- **The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions**. NeuroImage. Clinical. 2019 Academic Article +- **NeuroMeasure: A Software Package for Quantification of Cortical Motor Maps Using Frameless Stereotaxic Transcranial Magnetic Stimulation**. Frontiers in neuroinformatics. 2019 Academic Article +- **Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke**. Stroke. 2018 Academic Article +- **Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195**. PloS one. 2018 Academic Article +- **Impact of Focal White Matter Damage on Localized Subcortical Gray Matter Atrophy in Multiple Sclerosis: A 5-Year Study**. AJNR. American journal of neuroradiology. 2018 Academic Article\ +- **Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis**. NeuroImage. Clinical. 2018 Academic Article +- **White matter tract network disruption explains reduced conscientiousness in multiple sclerosis**. Human brain mapping. 2018 Academic Article +- **Combining Quantitative Susceptibility Mapping with Automatic Zero Reference (QSM0) and Myelin Water Fraction Imaging to Quantify Iron-Related Myelin Damage in Chronic Active MS Lesions**. AJNR. American journal of neuroradiology. 2017 Academic Article +- **Reduction of PK11195 uptake observed in multiple sclerosis lesions after natalizumab initiation**. Multiple sclerosis and related disorders. 2017 Academic Article +- **The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease**. Journal of Alzheimer's disease : JAD. 2017 Academic Article +- **Cognitive deficits in non-demented diabetic elderly appear independent of brain amyloidosis**. Journal of the neurological sciences. 2016 Academic Article +- **The application of a mathematical model linking structural and functional connectomes in severe brain injury**. NeuroImage. Clinical. 2016 Academic Article +- **Structural connectome disruption at baseline predicts 6-months post-stroke outcome**. Human brain mapping. 2016 Academic Article +- **Opportunities for Guided Multichannel Non-invasive Transcranial Current Stimulation in Poststroke Rehabilitation**. Frontiers in neurology. 2016 Academic Article +- **Profilometry: A new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis**. Human brain mapping. 2015 Academic Article +- **A mathematical simulation to assess variability in lung nodule size measurement associated with nodule-slice position**. Journal of digital imaging. 2015 Academic Article +- **Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study. Human brain mapping**. 2015 Academic Article +- **Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease**. Cell reports. 2015 Academic Article +- **Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis**. AJNR. American journal of neuroradiology. 2014 Conference Paper +- **Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter**. NeuroImage. 2014 Academic Article +- **Brainography: an atlas-independent surface and network rendering tool for neural connectivity visualization**. Neuroinformatics. 2014 Article +- **Spatial patterns of genome-wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain**. Human brain mapping. 2014 Academic Article +- **Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke**. Stroke. 2014 Academic Article +- **The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity**. Brain connectivity. 2013 Academic Article +- **Loss in connectivity among regions of the brain reward system in alcohol dependence**. Human brain mapping. 2012 Academic Article +- **Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution**. PloS one. 2012 Academic Article +- **A network diffusion model of disease progression in dementia**. Neuron. 2012 Academic Article +- **Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: the Loss in Connectivity (LoCo) score**. NeuroImage. 2012 Academic Article +- **The generation and validation of white matter connectivity importance maps**. NeuroImage. 2011 Academic Article GET IT + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Christophe-Giraud-.md b/Data/CU-DSS/CU-DSS-Christophe-Giraud-.md new file mode 100644 index 0000000..ad95897 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Christophe-Giraud-.md @@ -0,0 +1,123 @@ +--- +bio-current: + name-cn: + name_en: Christophe Giraud + email: + - cg582@cornell.edu + sex: # male/female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + + major: + title-raw: Adjunct Professor + title: # Associate Professor/Assistant Professor/Professor + interests: + - high-dimensional statistics + - fine modeling and statistics for Life Sciences (including Ecology, omics data and medicine) + homepage: + - # 如果有多个主页，请都填写上 + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/christophe-giraud/53f431cbdabfaedce54fd986 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University Paris 6 + school: University Paris 6 + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: École Normale Supérieure de Paris + school: École Normale Supérieure de Paris + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Christophe Giraud ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/christophe2.png?itok=bh4Q4La-) + +# Biography[English] +Christophe Giraud was a student of the École Normale Supérieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the École Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. + +He is Associate Editor for JASA (Journal American Statistical Association). + +# Biography[中文] + +# Interests[English] +- high-dimensional statistics +- fine modeling and statistics for Life Sciences (including Ecology, omics data and medicine) +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-David-Ruppert-.md b/Data/CU-DSS/CU-DSS-David-Ruppert-.md new file mode 100644 index 0000000..a89c6ea --- /dev/null +++ b/Data/CU-DSS/CU-DSS-David-Ruppert-.md @@ -0,0 +1,141 @@ +--- +bio-current: + name-cn: + name_en: David Ruppert + email: + - dr24@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: engineering + title-raw: Professor + title: Professor + interests: + - Data Mining + - Complex Systems, Network Science and Computation + - Health Systems + - Financial Engineering + - Statistics and Machine Learning + homepage: + - https://people.orie.cornell.edu/davidr/ + github: + googlescholar: + - https://scholar.google.com/citations?user=7fvHhzsAAAAJ + aminer: + - https://www.aminer.cn/profile/david-ruppert/56017ed345cedb3395e657aa + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Michigan State University + school: + email: + date-start: + date-end: 1977 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: University of Vermont + school: + date-start: + date-end: 1973 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Cornell University + school: + major: Mathematics + date-start: + date-end: 1970 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/in/dave-ruppert-0711454/ + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + - ASA + - IMS + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![David Ruppert ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Dave%20Ruppert_crop.jpg?itok=o86HBie6) + +# Biography[English] +David Ruppert is Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, and Professor of Statistics and Data Science, Cornell University. He received a BA in Mathematics from Cornell University in 1970, an MA in Mathematics from the University of Vermont in 1973, and a PhD in Statistics and Probability from Michigan State University in 1977. He was Assistant and then Associate Professor of Statistics at the University of North Carolina, Chapel Hill, from 1977 to 1987. He is a Fellow of the ASA and IMS and received the Wilcoxon Prize in 1986. Professor Ruppert was named "Highly cited" researcher by ISIHighlyCited.com and was ranked 21st in mathematics by journal citations. He has had 25 PhD students, many of them now leading researchers. Professor Ruppert has worked on stochastic approximation, transformations and weighting in regression, and smoothing. His current research focuses on astrostatistics, measurement error models, splines, semiparametric regression, and environmental statistics. He has published over 100 articles in refereed journals and has published five books, Transformation and Weighting in Regression, Measurement Error in Nonlinear Models (first and second editions), Semiparametric Regression, Statistics and Finance: An Introduction, and Statistics and Data Analysis for Financial Engineering. + +# Biography[中文] + +# Interests[English] +- Data Mining +- Complex Systems, Network Science and Computation +- Health Systems +- Financial Engineering +- Statistics and Machine Learning + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] +- Wilcoxon Prize,1986 + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- **Ruppert, David.** 2010. Statistics and Data Analysis for Financial Engineering. New York, NY:Springer. +- Carroll, R., **David Ruppert**, L. Stefanski, C Crainicanu. 2006. Measurement Error in Nonlinear Models, A Modern Perspective, 2nd Edition. (2) : 488. Chapman and Hall/CRC. +- **Ruppert, David.** 2004. Statistics and Finance: An Introduction. New York, NY: Springer. +- **Ruppert, David**, D. Wand, R Carroll. 2003. Semiparametric Regression. (12) : 404.Cambridge University Press. +- **Ruppert, David**, C. Shoemaker, Y. Wang, Y. Li, N Bliznyuk. 2012. "Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs." Journal of Agricultural, Biological, and Environmental Statistics 17 (4): 623-640. +- **Transformation and Weighting in Regression**, (1988), Chapman & Hall. (with R.J. Carroll) +- **Measurement Error in Nonlinear Models**, (1995), Chapman & Hall. (with R.J. Carroll and L.A. Stefanski) +- **Semiparametric Regression** D (2003), Cambridge University Press. (with R.J. Carroll and M.P. Wand ) +- **Statistics and Finance: An Introduction**, (2004), Springer + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-David-S.-Matteson-.md b/Data/CU-DSS/CU-DSS-David-S.-Matteson-.md new file mode 100644 index 0000000..dfb7ae8 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-David-S.-Matteson-.md @@ -0,0 +1,196 @@ +--- +bio-current: + name-cn: + name_en: David S. Matteson + email: + - matteson@cornell.edu + - dm484@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Associate Professor + title: Associate Professor + interests: + - Applied Biophysics + - Bayesian Analysis + - Biostatistics + - Dimension Reduction + - Disability + - Emergency Medical Services + - Financial Econometrics + - Functional Data Analysis + - Machine Learning + - Multivariate Statistics + - Neuroscience + - Nonparametrics + - Point Processes + - Semiparametrics + - Signal Processing + - Spatio-Temporal Modeling + - Sustainable Energy + - Time Series + homepage: + - http://www.stat.cornell.edu/~matteson/ + - https://davidsmatteson.com/ + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/david-s-matteson/53f4406edabfaee2a1d233de + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Chicago + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/find/stat/1/au:+Matteson_D/0/1/0/all/0/1 + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: + - ILR School[https://www.ilr.cornell.edu/] + - Computing and Information Science[https://cis.cornell.edu/] + - Center for Applied Mathematics[https://www.cam.cornell.edu/cam] + - Field of Operations Research[https://www.orie.cornell.edu/orie/people/gradfield.cfm] + - Program in Financial Engineering[https://www.orie.cornell.edu/research/financial_engineering.cfm] +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![David S. Matteson ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/dave_matteson_crop.jpg?itok=-6ZYQz8g) + +# Biography[English] +David S. Matteson is Associate Professor of Statistics and Data Science and at Cornell University, where he is a member of the ILR School, Computing and Information Science, the Center for Applied Mathematics, the Field of Operations Research, and the Program in Financial Engineering. Professor Matteson received his PhD in Statistics from the University of Chicago and his BSB in Finance, Mathematics, and Statistics from the University of Minnesota. He received a CAREER Award from the National Science Foundation (2015) and a Faculty Research Award from the Xerox/PARC Foundation (2014), and he and his students have received numerous Best Paper Awards. + +He is currently an Associate Editor of the Journal of the American Statistical Association-Theory & Methods, The American Statistician, Statistica Sinica, and the Journal of Econometrics, and formerly for Biometrics. He is an elected Officer for the Business and Economic Statistics Section of the American Statistical Association, a member of the Institute of Mathematical Statistics, the International Biometric Society, the International Society for Bayesian Analysis, and the American Geophysical Union. He is coauthor of Statistics and Data Analysis for Financial Engineering, and MOOC instructor for Introduction to Time Series Analysis. + +He is lead PI and Director of the NSF funded PRISM Institute for Trans-domain Systemic Risk, lead PI and co-Director of the NSF funded TRIPODS Greater Data Science Cooperative Institute (GDSC), co-PI of the NSF funded Atomic-Level Structural Dynamics in Catalysts Institute, co-PI of a USAID funded Feed-the-Future team, an Executive Committee Member for the Cornell Center for Data Science for Enterprise & Society, and a Visiting Scholar at the Institute for Mathematics and its Applications. + +# Biography[中文] + +# Interests[English] +* Applied Biophysics +* Bayesian Analysis +* Biostatistics +* Dimension Reduction +* Disability +* Emergency Medical Services +* Financial Econometrics +* Functional Data Analysis +* Machine Learning +* Multivariate Statistics +* Neuroscience +* Nonparametrics +* Point Processes +* Semiparametrics +* Signal Processing +* Spatio-Temporal Modeling +* Sustainable Energy +* Time Series + +# Interests[中文] + +# Education[English] +- 2008 PhD : Statistics at the University of Chicago +- 2003 BSB in Finance: Mathematics, and Statistics at the University of Minnesota + +# Education[中文] + +# Awards[English] +- **CAREER Award from the National Science Foundation** (2015) +- **Faculty Research Award from the Xerox/PARC Foundation** (2014) + + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Associate Professor: Statistics and Data Science +- Social Statistics at Cornell University +# Work experience[中文] + +# Publication[English] +- Risk, B., **Matteson, D.S.** and Ruppert, D. (2015), “Likelihood Component Analysis.” +- Tupper, L., **Matteson, D.S.**, and Anderson, C.L. (2015), “Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior.” +- James, N.A. and **Matteson, D.S.** (2015), “Change Points via Probabilistically Pruned Objectives.” +- Zhou, Z., and **Matteson, D.S.**(2015), “Predicting Melbourne Ambulance Demand Using Kernel Warping.” +- Nicholson, W.B., **Matteson, D.S.** and Bien, J. (2015), “VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables.” +- James, N.A., Kejariwal, A. and **Matteson, D.S.** (2015), “Leveraging Cloud Data to Mitigate User Experience from Breaking Bad: The Twitter Approach.” +- Nicholson, W.B., Bien, J. and **Matteson, D.S.** (2015), “HVAR: High Dimensional Forecasting via Interpretable Vector Autoregression.” +- Kowal, D.R., **Matteson, D.S.** and Ruppert, D. (2015), “A Bayesian Multivariate Functional Dynamic Linear Model.” +- **Matteson, D.S.** and Tsay, R.S. (2015), “Independent Component Analysis via Distance Covariance,” To Appear, Journal of the American Statistical Association. +- Westgate, B.S., Woodard, D.B., **Matteson, D.S.** and Henderson, S.G. (2015), “Large-Network Travel Time Estimation for Ambulance Fleet Management,” To Appear, European Journal of Operational Research. +- Zhou, Z., **Matteson, D.S.**, Woodard, D.B., Micheas, A.C. and Henderson, S.G. (2015), “A Spatio-Temporal Point Process Model for Ambulance Demand,” Journal of the American Statistical Association, Vol. 110, No. 509, 6-15. +- Zhou, Z., and **Matteson, D.S.** (2015), “Predicting Ambulance Demand: A Spatio-Temporal Kernel Approach,” Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2297-2303. +- James, N.A. and **Matteson, D.S.** (2015), “ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data,” Journal of Statistical Software, Vol. 62, No. 7: 1-25. +- **Matteson, D.S.** and James, N.A. (2014), “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data,” Journal of the American Statistical Association, Vol. 109, No. 505, 334-345. +- Risk, B., **Matteson, D.S.**, Ruppert, D., Eloyan, A. and Cao, B. (2014), “An Evaluation of Independent Component Analyses with an Application to Resting State fMRI,” Biometrics, Vol. 70, No. 1: 224-236. +- Erickson, W.A., von Schrader, S., Bruyre, S., VanLooy, S., and **Matteson, D.S.** (2014) “Disability-Inclusive Employer Practices and Hiring of Individuals with Disabilities,” Rehabilitation Research, Policy, and Education, Vol. 28, No. 4, 309-328. +- **Matteson, D.S.**, James, N.A., Nicholson, W.B. and Segalini, L.C. (2013), “Locally Stationary Vector Processes and Adaptive Multivariate Modeling,” Acoustics, Speech and Signal Processing, IEEE, 8722-8726. +- Westgate, B.S., Woodard, D.B., **Matteson, D.S.** and Henderson, S.G. (2013), “Travel Time Estimation for Ambulances using Bayesian Data Augmentation,” Annals of Applied Statistics, Vol. 7, No. 2, 1139-1161. +- Holan, S.H., Yang, W.-H., **Matteson, D.S.** and Wikle, C.K. (2012), “An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 485-499. +- Holan, S.H., Yang, W.-H., **Matteson, D.S.** and Wikle, C.K. (2012), “Rejoinder, An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 504-505. +- **Matteson, D.S.** and Ruppert, D. (2011), “GARCH Models of Dynamic Volatility and Correlation,” Signal Processing Magazine, IEEE, Vol. 28, No. 5, 72-82. +- Woodard, D.B., **Matteson, D.S.** and Henderson S.G. (2011), “Stationarity of Generalized Autoregressive Moving Average Models,” Electronic Journal of Statistics, Vol. 5, No. 0, 800-828. +- **Matteson, D.S.**, McLean, M.W., Woodard, D.B. and Henderson, S.G. (2011), “Forecasting Emergency Medical Service Call Arrival Rates,” Annals of Applied Statistics, Vol. 5, No. 2B, 1379-1406. +- **Matteson, D.S.** and Tsay, R.S. (2011), “Dynamic Orthogonal Components for Multivariate Time Series,” Journal of the American Statistical Association, Vol. 106, No. 496, 1450-1463. +- **Matteson, D.S.** and Tsay, R.S. (2007), “High Dimensional Volatility Models,” JSM Proceedings, Business and Economics Statistics Section, Alexandria, VA: American Statistical Association, 1006-1013. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Dominique-Fourdrinier-.md b/Data/CU-DSS/CU-DSS-Dominique-Fourdrinier-.md new file mode 100644 index 0000000..23fc653 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Dominique-Fourdrinier-.md @@ -0,0 +1,196 @@ +--- +bio-current: + name-cn: + name_en: Dominique Fourdrinier + email: + - df274@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Adjunct Professor + title: # Associate Professor/Assistant Professor/Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - # 如果有多个主页，请都填写上 + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.researchgate.net/profile/Dominique-Fourdrinier + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/search/stat?searchtype=author&query=Fourdrinier%2C+D + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: Université de Rouen + school: Département de Mathématiques + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Dominique Fourdrinier ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/fourdrinier.jpg?itok=3t71bemW) + +# Biography[English] + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- Covariance matrix estimation under data–based loss(May 2021),**Dominique Fourdrinier**,Anis M. Haddouche,Fatiha Mezoued +- Estimation of the inverse scatter matrix for a scale mixture of Wishart matrices under Efron-Morris type losses(Apr 2021),Djamila Boukehil,**Dominique Fourdrinier**,Fatiha Mezoued,William Strawderman +- Covariance matrix estimation under data-based loss(Dec 2020),Anis M. Haddouche,**Dominique Fourdrinier**,Fatiha Mezoued +- Scale matrix estimation of an elliptically symmetric distribution in high and low dimensions(Sep 2020),Anis M. Haddouche，**Dominique Fourdrinier**，Fatiha Mezoued +- Scale matrix estimation under data-based loss in high and low dimensions(May 2020),Anis M. Haddouche,**Dominique Fourdrinier**,Fatiha Mezoued +- On efficient prediction and predictive density estimation for normal and spherically symmetric models(Feb 2019),**Dominique Fourdrinier**,Éric Marchand,William Strawderman +- On efficient prediction and predictive density estimation for spherically symmetric models(Jan 2019),**Dominique Fourdrinier**,Éric Marchand,William Strawderman +- Sharp non asymptotic oracle inequalities for non parametric computerized tomography model(Nov 2018),**Dominique Fourdrinier**,Sergey Pergamenshchikov +- On efficient prediction and predictive density estimation for spherically symmetric models(Jul 2018),**Dominique Fourdrinier**,Éric Marchand,William Strawderman +- Covariance matrix estimation of an elliptically symmetric distribution in high dimensional setting(Jun 2018),Anis M. Haddouche，Fatiha Mezoued，**Dominique Fourdrinier** +- Estimation of a Mean Vector for Spherically Symmetric Distributions I: Known Scale(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Decision Theory Preliminaries(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Spherically Symmetric Distributions(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Estimation of a Normal Mean Vector I(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Restricted Parameter Spaces(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Estimation of a Normal Mean Vector II(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Loss and Confidence Level Estimation(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Estimation of a Mean Vector for Spherically Symmetric Distributions II: With a Residual(Jan 2018),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Unbiased risk estimates for matrix estimation in the elliptical case(Apr 2017),Stephane Canu,**Dominique Fourdrinier** +- A Bayes minimax result for spherically symmetric unimodal distributions(Feb 2016),**Dominique Fourdrinier**,Fatiha Mezoued,William Strawderman +- Stokes’ theorem, Stein’s identity and completeness(Nov 2015),**Dominique Fourdrinier**,William Strawderman +- Estimation of the inverse scatter matrix of an elliptically symmetric distribution(Sep 2015),**Dominique Fourdrinier**,Fatiha Mezoued,Martin T. Wells +- Robust minimax Stein estimation under invariant data-based loss for spherically and elliptically symmetric distributions(May 2015),**Dominique Fourdrinier**,William Strawderman +- On completeness of the general linear model with spherically symmetric errors(Sep 2014),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- On the non existence of unbiased estimators of risk for spherically symmetric distributions(Aug 2014),**Dominique Fourdrinier**,William Strawderman +- Akaike's Information Criterion, Cp and Estimators of Loss for Elliptically Symmetric Distributions(Jun 2014),Aurélie Boisbunon,Stephane Canu,**Dominique Fourdrinier**,Martin T. Wells +- AIC and Cp as estimators of loss for spherically symmetric distributions(Aug 2013),Aurélie Boisbunon,Stephane Canu,**Dominique Fourdrinier**,Martin T. Wells +- Generalized bayes minimax estimators of location vectors for spherically symmetric distributions with residual vector(Apr 2013),**Dominique Fourdrinier**,Kortbi Othmane,William Strawderman +- AIC, Cp and estimators of loss for elliptically symmetric distributions(Apr 2013),Aurélie Boisbunon,Stephane Canu,**Dominique Fourdrinier**,Martin T. Wells +- Bayes minimax estimation under power priors of location parameters for a wide class of spherically symmetric distributions(Jan 2013),**Dominique Fourdrinier**,Fatiha Mezoued,William Strawderman +- On Improved Loss Estimation for Shrinkage Estimators(Mar 2012),**Dominique Fourdrinier**,Martin T. Wells +- A weak functional framework for applications in statistics(Jan 2012),Blouza Adel,**Dominique Fourdrinier**,Patrice Lepelletier +- Bayes minimax estimators of a location vector for densities in the Berger class(Jan 2012),**Dominique Fourdrinier**,Fatiha Mezoued,William Strawderman +- Criteria for variable selection with dependence(Sep 2011),Aurélie Boisbunon,Stephane Canu,**Dominique Fourdrinier** +- On improved predictive density estimation with parametric constraints(Jan 2011),**Dominique Fourdrinier**,Éric Marchand,Ali Righi,William Strawderman +- On Bayes estimators with uniform priors on spheres and their comparative performance with maximum likelihood estimators for estimating bounded multivariate normal means(Jul 2010),**Dominique Fourdrinier**,Éric Marchand +- Robust generalized Bayes minimax estimators of location vectors for spherically symmetric distribution with unknown scale(Jan 2010),**Dominique Fourdrinier**,William Strawderman +- Truncated sequential estimation of the parameter of a first order autoregressive process with dependent noises(Mar 2009),**Dominique Fourdrinier**,V. Konev,S. Pergamenshchikov +- Estimation of a mean vector under quartic loss(Dec 2008),**Dominique Fourdrinier**,Idir Ouassou,William Strawderman +- A unified and generalized set of shrinkage bounds on minimax Stein estimates(Nov 2008),**Dominique Fourdrinier**,William Strawderman +- Generalized Bayes minimax estimators of location vectors for spherically symmetric distributions(Apr 2008),**Dominique Fourdrinier**,William Strawderman +- Estimating a general function of a quadratic function(Feb 2008)**Dominique Fourdrinier**,Patrice Lepelletier +- Bayes minimax estimators of the mean of a scale mixture of multivariate normal distributions(Jan 2008),**Dominique Fourdrinier**,Kortbi Othmane,William Strawderman +- Improved estimation for elliptically symmetric distributions with unknown block diagonal covariance matrix(Jan 2008),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Improved Model Selection Method for a Regression Function with Dependent Noise(Feb 2007),**Dominique Fourdrinier**,S. Pergamenshchikov +- Estimation of a Location Parameter with Restrictions or “vague information” for Spherically Symmetric Distributions(Feb 2006),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- On the inevitability of a paradox in shrinkage estimation for scale mixtures of normals(Mar 2004),**Dominique Fourdrinier**,Éric Marchand,William Strawderman +- Estimation améliorée explicite d'un degré de confiance conditionnel(Dec 2003),**Dominique Fourdrinier**,Patrice Lepelletier +- Estimation of a parameter vector when some components are restricted(Jul 2003),**Dominique Fourdrinier**,Idir Ouassou,William Strawderman +- Robust shrinkage estimation for elliptically symmetric distributions with unknown covariance matrix(Feb 2003),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- On Bayes and unbiased estimators of loss(Feb 2003),**Dominique Fourdrinier**,William Strawderman +- Estimation under ℓ1-Symmetry(Nov 2002),**Dominique Fourdrinier**,Anne-Sophie Lemaire +- Statistique inférentielle(Jan 2002),**Dominique Fourdrinier** +- Estimation of the mean of a spherically symmetric distribution with constraints on the norm(Jun 2000),**Dominique Fourdrinier**,Idir Ouassou +- Bayesian Estimation for Spherically Symmetric Distributions(Jul 1999),Dominique Cellier,**Dominique Fourdrinier**,Martin T. Wells +- Non trivial solutions of nonlinear partial differential inequations and order cut-off(Jan 1999),D. Blanchard,**Dominique Fourdrinier** +- Estimation robuste pour des lois à symétrie elliptique à matrice de covariance inconnue(May 1998),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Estimation de la moyenne d'une loi 1-exponentielle multidimensionnelle(Apr 1998),**Dominique Fourdrinier**,Anne-Sophie Lemaire +- Estimation of the mean of a l1-Exponential Multivariate Distribution(Apr 1998),**Dominique Fourdrinier**,Anne-Sophie Lemaire +- On the construction of Bayes minimax estimators(Apr 1998),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Sur les estimateurs de Bayes minimax: une méthode constructive(May 1997),**Dominique Fourdrinier**,William Strawderman,Martin T. Wells +- Estimation of a non-centrality parameter under Stein-type losses(Feb 1996),**Dominique Fourdrinier**,Anne Philippe,C.P. Robert +- A Paradox Concerning Shrinkage Estimators: Should a Known Scale Parameter Be Replaced by an Estimated Value in the Shrinkage Factor?(Feb 1996),**Dominique Fourdrinier**,William Strawderman +- A paradox concerning estimation: that a known parameter should be estimated(Jan 1996),**Dominique Fourdrinier**,William Strawderman +- Shrinkage Positive Rule Estimators for Spherically Symmetrical Distributions(May 1995),D. Cellier,**Dominique Fourdrinier**,William Strawderman +- Loss Estimation for Spherically Symmetrical Distributions(May 1995),**Dominique Fourdrinier**,Martin T. Wells +- Intrinsic losses for empirical Bayes estimation: A note on normal and Poisson cases(Apr 1995),**Dominique Fourdrinier**,Christian P. Robert +- Estimation of a Loss Function for Spherically Symmetric Distributions in the General Linear Model(Apr 1995),**Dominique Fourdrinier**,Martin T. Wells +- Shrinkage Estimators under Spherical Symmetry for the General Linear Model(Feb 1995),D. Cellier,**Dominique Fourdrinier** +- Comparisons of selection rules of a regression model: a decision approach(Jan 1994),**Dominique Fourdrinier**,Martin T. Wells +- Estimation du paramètre de position d’une loi à symétrie sphérique: Une condition générale de domination de l’estimateur des moindres carrés. (Estimation of the location parameter of a spherically symmetric distribution: A general condition of domination of the least- square estimator)(Jan 1992),Dominique Cellier,**Dominique Fourdrinier** +- Estimation d’un coût quadratique pour des lois à symétrie sphérique. (Estimation of a quadratic loss under spherically symmetric distribution)(Jan 1992),**Dominique Fourdrinier**,Martin T. Wells +- Sur les lois a symétrie elliptique. (On the laws with elliptic symmetry)(Jan 1990),Dominique Cellier,**Dominique Fourdrinier** +- Robust shrinkage estimators of the location parameter for elliptically symmetric distributions(Apr 1989),Dominique Cellier,**Dominique Fourdrinier**,Christian Robert +- Estimateurs à rétrécisseur du paramètre de position d’une loi à symétrie sphérique. (Shrinkage estimators of the location parameter for spherically symmetric distributions)(Jan 1987),Dominique Cellier,**Dominique Fourdrinier**,Christian Robert +- Estimateurs à rétrécisseur avec coût quadratique général et fonction de rétrécissement non nécessairement continue (Shrinkage estimators with general quadratic loss and shrinkage function which is not necessarily continuous)(Jan 1986),Dominique Cellier,**Dominique Fourdrinier** +- Estimateurs a rétrécisseurs de la moyenne d’une loi normale multidimensionnelle, pour un coût quadratique général. (Shrinkage estimators of the mean of a multidimensional normal law under general quadratic loss)(Jan 1985),Dominique Cellier,**Dominique Fourdrinier** +- Statistique et analyse des données,Gérard Grancher,**Dominique Fourdrinier** +- Critères robustes de sélection de variables pour le modèle linéaire via l'estimation de coût(Sep 2011),Aurélie Boisbunon,Stephane Canu,**Dominique Fourdrinier** +- Advances in modern statistical theory and applications. A Festschrift in honor of Morris L. Eaton,**Dominique Fourdrinier**,Éric Marchand,Andrew L. Rukhin + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Elizabeth-Karns-.md b/Data/CU-DSS/CU-DSS-Elizabeth-Karns-.md new file mode 100644 index 0000000..de862dc --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Elizabeth-Karns-.md @@ -0,0 +1,163 @@ +--- +bio-current: + name-cn: + name_en: Elizabeth Karns + email: + - karns@cornell.edu + sex: female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - ILR school[https://www.ilr.cornell.edu/] + major: + title-raw: Senior Lecture + title: # Associate Professor/Assistant Professor/Professor + interests: + - Harassment prevention + - Law and policiesOccupational + - Safety and Health + - Problem solving + - Rights of working people + - Statistical Theory, Methods, Analysis + homepage: + - https://www.ilr.cornell.edu/people/m-karns + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/elizabeth-karns/53f43b49dabfaee02acfab66 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Quinnipiac University + school: School of Law + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: Reed College + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.researchgate.net/scientific-contributions/M-Elizabeth-Karns-2043652286 + research: + software: + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/in/m-elizabeth-karns/ + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Elizabeth Karns ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/karns.jpg?itok=TP88O20C) + +# Biography[English] +Liz Karns is a senior lecturer of social statistics in Cornell's ILR School. +Liz Karns is an epidemiologist and lawyer. Her research interests involve worker health and safety, ethical responsibilities in the conduct data analysis, and the economic consequences of sexual assault and harassment. Her practice has ranged from individual juvenile clients large multi-national corporations. She is interested in fostering an intellectual environment for students that integrates science, law, and societal needs. + + +# Biography[中文] + +# Interests[English] +* Harassment prevention +* Law and policiesOccupational +* Safety and Health +* Problem solving +* Rights of working people +* Statistical Theory, Methods, Analysis + +# Interests[中文] +* Harassment prevention +* Law and policiesOccupational +* Safety and Health +* Problem solving +* Rights of working people +* Statistical Theory, Methods, Analysis +# Education[English] + +# Education[中文] + +# Awards[English] +- Robert N. Stern Mentoring Award 2013, ILR. 2013 +- MacPherson Teaching Award, ILR. 2009 + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Adding It Up: Hidden Cost of Sexual Assault. Presented to KAPI Pre-Law Fraternity. Cornell University. 2017. +- Adding it Up: Lifetime Costs of Sexual Assault and SUNY Binghamton Rates. Presented to SUNY - Binghamton. Social Work Department. 2017. +- Adding it Up: Hidden Costs of Sexual Assault and UC-Berkeley Rates. Presented to Gender Equity Center. University of California, Berkeley, . 2017. +- Economic Consequences of Sexual Assault. Presented to Cornell University. Ithaca NY. 2016. +- Negotiating First Salaries. Presented to Cornell Women's Resource Center. Willard Straight Hall. 2016. +- Sexual Assault Reporting Rate Differences Across Campuses. Presented to Cornell SAAW. 2016. +- Identifying Barriers to Reporting Sexual Assault. Presented to Keeton House Residential Life. Keeton House. 2016. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell History Dept.. McGraw Hall. 2016. +- Evaluating the Gap in Sexual Assault Reporting: Survey & Clery Data. Presented to CSVP. Scholkopf. 2016. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell Residence Life. Appel. 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell West Campus Bethe. Bethe House. 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell Librarians -- Hotel/ILR/Johnson. Statler . 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell Radio. ILR. 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to PALS. Stimson Hall. 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to CSVP. Clara Dickson. 2015. +- Measuring Change: Sexual Assault Reporting Rates Analysis. Presented to Gannett. Gannett. 2015. +- Cornell Sexual Misconduct & Harassment Survey. Presented to Cornell Womens Resource Center. Willard Straight Hall. 2015. +- Measuring Change: Sexual Assault Reporting Rates Analysis. Presented to Gannett Health. ILR. 2015. +- Ethics of Data Analysis. Presented to Translational Research Center. Brofenbrenner Center. 2015. +- Statistical Evidence in Employment Discrimination. Presented to ILR Labor and Employment Law Extension. ILR NYC. 2010. + +# Work experience[中文] + +# Publication[English] +- **Does Physician Performance Explain Interspecialty Differences in Malpractice Claim Rates?**, Article, Aug 1994, Mark Taragin, Frank A Sonnenberg,**M E Karns**, Jeffrey Carson +- **Physician Demographics and the Risk of Medical Malpractice**, Article, Aug 1993, Mark Taragin, Adam P. Wilezek, **M. Elizabeth Karns**, Jeffrey L Carson +- **Physician demographics and the risk of medical malpractice**, Article, Nov 1992, Mark Taragin,Adam P. Wilczek, M.Elizabeth Karns, Jeffrey Carson +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Florentina-Bunea-.md b/Data/CU-DSS/CU-DSS-Florentina-Bunea-.md new file mode 100644 index 0000000..427c2d1 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Florentina-Bunea-.md @@ -0,0 +1,203 @@ +--- +bio-current: + name-cn: + name_en: Florentina Bunea + email: + - fb238@cornell.edu + sex: female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Statistics And Data Science + title-raw: Professor + title: Professor + interests: + - Foundations of data science; statistical machine learning theory. + - High dimensional statistical inference in parametric and non-parametric models:regression,covariance estimation, graphical models, model-based clustering (hard clustering and overlapping clustering), cluster-based models, latent variable models, topic models, networks,new perspectives in prediction problems, comparison of clustering schemes in Wassersteindistance. + - Applications to Immunology, Systems Biology, Genetics,Neuroscience and other areas + homepage: + - https://bunea.stat.cornell.edu/ + github: + googlescholar: + - https://scholar.google.com/citations?user=vQrFgocAAAAJ + aminer: + - https://www.aminer.cn/profile/florentina-bunea/53f46297dabfaeb22f52d437 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Washington + school: + email: + date-start: + date-end: 2000 + advisor: Jon A. Wellner. + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: University of Bucharest + school: + date-start: + date-end: 1991 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: University of Bucharest + school: + major: Mathematics + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/search/stat?searchtype=author&query=Bunea%2C+F + linkedin: + - https://www.linkedin.com/in/florentina-bunea-19382440 + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: + - Center for Applied Mathematics (CAM) + - Machine Learning Group in CIS + - Machine Learning Group in the Computing and Information +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: Cornell University + school: + - Department of Statistics and Data Science + major: + - Statistics And Data Science + email: + - fb238@cornell.edu + homepage: + - https://bunea.stat.cornell.edu/ + date-start: 2011 + title: Professor + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Florentina Bunea ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Florentina%20BuneaCrop.jpg?itok=9U1cMeZb) + +# Biography[English] +I am a faculty member of the Department of Statistics and Data Science, a member of the Center for Applied Mathematics (CAM) and a member of the Machine Learning Group in CIS. As a member of the CIS Diversity and Inclusion Council, I am committed to promoting the diversity of the work force in data-science disciplines. + +My research is broadly centered on statistical machine learning theory and high-dimensional statistical inference. I am interested in developing new methodology accompanied by sharp theory for solving a variety of problems in data science. Recent research projects include high-dimensional latent-space clustering, cluster-based inference, network modeling, inference in high dimensional models with hidden latent structure and topic models. I continue to be interested in the general areas of model selection, sparsity and dimension reduction in high dimensions, and in applications to genetics, systems immunology, neuroscience, sociology, among other disciplines. + +My research is funded in part by the National Science Foundation (NSF-DMS). I am a Fellow of the Institute of Mathematical Statistics (IMS). I have served or am currently serving as an Associate Editor for a number of journals (the Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, the Annals of Applied Statistics). +# Biography[中文] + +# Interests[English] +* Foundations of data science; statistical machine learning theory. +* High dimensional statistical inference in parametric and non-parametric models: regression,covariance estimation, graphical models, model-based clustering (hard clustering and overlapping clustering), cluster-based models, latent variable models, topic models, networks,new perspectives in prediction problems, comparison of clustering schemes in Wassersteindistance. +* Applications to Immunology, Systems Biology, Genetics,Neuroscience and other areas +# Interests[中文] + +# Education[English] +- Ph.D. in Statistics, University of Washington, Seattle, 2000. +Advisor: Jon A. Wellner. +- B.S./M.S. in Mathematics, First Class, University of Bucharest, Romania, 1991 + +# Education[中文] + +# Awards[English] +- NSF-DMS award 2015195, PI, Learning from hidden signatures in high dimensional models, +2020 - 2023. +- NSF-DMS award 1712709, PI, Statistical foundations of model-based variable clustering, 2017 - 2020. +- NSF-DMS award 1310119, co-PI, Estimation of high dimensional matrices of low effective rank with applications to structural copula models, 2013 - 2016. +- NSF-DMS award 10007444, PI, Matrix estimation under rank constraints for complete and +incomplete noisy data, 2010 - 2013. +- (NSF-DMS) award 0925275, PI, conference grant, From Probability to Statistics and Back: +High-Dimensional Models and Processes, Seattle, July 28 - 31, 2010. +- (NSF-DMS) award 0706829, co-PI: Sparsity oracle inequalities via l1 regularization in nonparametric models, 2007 - 2010. +- (NSF-DMS) 0406049, PI: Curve aggregation and classification, 2004 - 2007 +- Elected Fellow of the Institute of Mathematical Statistics (IMS), 2017. +- Fellow of the Newton Institute, Cambridge University, UK, 2008, 2016, 2018. +- SAMSI fellow, Spring 2014. +- Invited Professor, Universit´e de Paris VI and CREST, France, 2003, 2009 (Host: Alexandre +Tsybakov.). +- Invited Visiting Scholar, The John Hopkins School of Public Health, Jan/Feb 2008. +- Invited Researcher, L’Institut Henri Poincar´e, Paris, France, April - July 2001. (Host: Lucien +Birg´e.) +- The Centre National de la Recherche Scientifique (CNRS) Award in Statistics for young +researchers, 2001 (CNRS is the French equivalent of the National Science Foundation.) + +# Awards[中文] + +# Talks[English] +- Elected Fellow of the Institute of Mathematical Statistics (IMS), 2017. +- Fellow of the Newton Institute, Cambridge University, UK, 2008, 2016, 2018. +- SAMSI fellow, Spring 2014. +- Invited Professor, Universit´e de Paris VI and CREST, France, 2003, 2009 (Host: Alexandre +Tsybakov.). +- Invited Visiting Scholar, The John Hopkins School of Public Health, Jan/Feb 2008. +- Invited Researcher, L’Institut Henri Poincar´e, Paris, France, April - July 2001. (Host: Lucien +Birg´e.) +- The Centre National de la Recherche Scientifique (CNRS) Award in Statistics for young +researchers, 2001 (CNRS is the French equivalent of the National Science Foundation.) + + +# Talks[中文] + +# Work experience[English] +* Professor: Cornell University, Department of Statistics and Data Science, 2011 - + – Faculty member of the Center for Applied Mathematics (CAM). + – Faculty member of the Machine Learning Group in the Computing and Information + College. + – Faculty member of the Advisory Council for Data Science for Enterprise and Society. + – Member of the CIS Council on Diversity and Inclusion. +* Assistant/Associate Professor: + Florida State University, Department of Statistics, 2000- 2011. +* Research/Teaching Assistant: + University of Washington, Department of Statistics, September 1995 - August 2000; University Politehnica Bucharest, Department of Mathematics,September 1991 - September 1995. +# Work experience[中文] + +# Publication[English] +- **Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations**, (2021+), Xin Bing, **Florentina Bunea**, Seth Strimas-Mackey and Marten Wegkamp. +- **Prediction in latent factor regression: Adaptive PCR and beyond**(2021+), Xin Bing, **Florentina Bunea**, Seth Strimas-Mackey and Marten Wegkamp. Journal of Machine Learning Research [ArXiv]. +- **Inference in latent factor regression with clusterable features**(2021+), Xin Bing, **Florentina Bunea** and Marten Wegkamp. Bernoulli [ArXiv]. +- **Essential Regression – a generalizable framework for inferring causal latent factors from multi-omic human datasets** (2021+), Xin Bing, Tyler Lovelace, **Florentina Bunea**, Marten Wegkamp, Harinder Singh, Panayiotis V Benos, Jishnu Das. Submitted. +- **Detecting approximate replicate components of a high-dimensional random vector with latent structure** (2020), Xin Bing, **Florentina Bunea** and Marten Wegkamp. Submitted. [ArXiv]. +- **Interpolation under latent factor regression models** (2020+), **Florentina Bunea**, Seth Strimas-Mackey and Marten Wegkamp. Submitted.[ArXiv]. +- **Optimal estimation of sparse topic models** (2020), Xin Bing, **Florentina Bunea**, Marten Wegkamp. Journal of Machine Learning Research, Vol. 21, 1-45. [ArXiv]. +- **A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics** (2020), Xin Bing, **Florentina Bunea** and Marten Wegkamp. Bernoulli, Vol. 26 (3), 1765-1796. [ArXiv] (Python code is coming soon. For the beta-version of the code, please contact xb43@cornell.edu) +- **Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering** (2020), Xin Bing, **Florentina Bunea**, Yang Ning and Marten Wegkamp. The Annals of Statistics, Vol. 48(4), 2055-2081. [ArXiv] (R-package is coming up soon. For the beta-version of the code, please contact xb43@cornell.edu) +- **High-Dimensional Inference for Cluster-Based Graphical Models** (2020), C. Eisenach, **F. Bunea**, Y. Ning and C. Dinicu, Journal of Machine Learning Research, Vol. 21, 1- 55. [ArXiv]. +- **Model-assisted variable clustering: minimax-optimal recovery and algorithms** (2020), **Florentina Bunea**, Christophe Giraud, Xi Luo, Martin Royer and Nicolas Verzelen, The Annals of Statistics, Vol. 48 (1), 111-137. [ArXiv]. +- **Essential Regression** (2019), Xin Bing, **Florentina Bunea**, Marten Wegkamp and Seth Strimas-Mackey. [ArXiv]. +- **Latent model-based clustering for biological discovery** (2019), Xin Bing, **Florentina Bunea**, Martin Royer, Jishnu Das. iScience ISSN 2589-0042. +- **PECOK: a convex optimization approach to variable clustering** (2017), **Florentina Bunea**, Christophe Giraud, Martin Royer, and Nicolas Verzelen. [Arxiv]. +- **Minimax Optimal Variable Clustering in G-models via Cord** (2016), **Florentina Bunea**, Christophe Giraud and Xi Luo. [Arxiv]. +- **Convex banding of the covariance matrix** (2016), J. Bien, **F. Bunea** and L. Xiao, Journal of the American Statistical Association,Volume 111, 834-845. [ArXiv] +- **On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA** (2015), **F. Bunea** and L. Xiao, Bernoulli, Vol. 21, 1200-1230. [ArXiv] +- **The square root group lasso: theoretical properties and fast algorithms** (2014), **F. Bunea**, J. Lederer and Y. She, IEEE-Information Theory, Vol. 60, 1313-1325, [ArXiv]; +- **Joint variable and rank selection for parsimonious estimation of high dimensional matrices**, (2012), **F. Bunea**, Y. She and M. Wegkamp, The Annals of Statistics, Vol. 40, 2359-2388, [ArXiv] +- **Optimal selection of reduced rank estimators of high-dimensional matrices** (2011), **F. Bunea**, Y. She and M. Wegkamp, The Annals of Statistics, Vol. 39, 1282 – 1309, [ArXiv]; +- **Spades and Mixture Models** (2010), **F. Bunea**, M. Wegkamp, A. Tsybakov and A. Barbu, The Annals of Statistics, Vol. 38, No. 4, 2525 – 2558, [ArXiv] +- **Honest variable selection in linear and logistic regression models via l1 and l1 + l2 penalization** (2008), **F. Bunea**, The Electronic Journal of Statistics , Vol. 2, Pages: 1153-1194 .[ArXiv] +- **Aggregation for Gaussian Regression** (2007), **F. Bunea**, M. Wegkamp and A. Tsybakov, The Annals of Statistics, 35 (4), 1674 – 1697. [ArXiv] +- **Sparsity oracle inequalities for the lasso** (2007), **F. Bunea**, A. Tsybakov and M. Wegkamp, The Electronic Journal of Statistics, 169 – 194. [ArXiv] +- **Consistent Covariate Selection and Post Model Selection Inference in Semiparametric Regression** (2004), **F. Bunea**, The Annals of Statistics, Vol. 32, No. 3, 898-927. [ArXiv] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Francesca-Molinari-.md b/Data/CU-DSS/CU-DSS-Francesca-Molinari-.md new file mode 100644 index 0000000..073668f --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Francesca-Molinari-.md @@ -0,0 +1,156 @@ +--- +bio-current: + name-cn: + name_en: Francesca Molinari + email: + - fm72@cornell.edu + sex: female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Economics + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://molinari.economics.cornell.edu/ + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/francesca-molinari/53f46201dabfaedf436349a6 + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: 2019-x-x +edu-phd: # 读博经历 + university: Northwestern University + school: + email: + date-start: 1998 + date-end: 2003 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: CORIPE Piemonte + school: + date-start: 1997 + date-end: 1998 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Università degli Studi di Torino + school: + major: Economia + date-start: 1993 + date-end: 1997 +page-other: + publication: + - https://molinari.economics.cornell.edu/publications.html + research: + - https://molinari.economics.cornell.edu/research.html + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Francesca Molinari ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/francesca-molinari_crop.jpg?itok=7rxbabYa) + +# Biography[English] +I am the H. T. Warshow and Robert Irving Warshow Professor in the Department of Economics at Cornell University. I received my Ph.D. from the Department of Economics at Northwestern University, after obtaining a BA and Masters in Economics at the Università degli Studi di Torino (Italy). My research interests are in econometrics, both theoretical and applied. My theoretical work is concerned with the study of identification problems, and with proposing new methods for statistical inference in partially identified models. In my applied work I have focused primarily on the analysis of decision making under risk and uncertainty. I have worked on estimation of risk preferences using market level data, and on the analysis of individuals' probabilistic expectations using survey data. + +Francesca Molinari is a professor in the economics department. Her research focuses on econometric theory and applied econometrics. She got her PhD in Economics from Northwestern University and her BS in Economics from the Universita degli Studi di Torino. +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] +- 1998-2003 Northwestern University, Ph.D. in Economics +- 1997-1998 CORIPE Piemonte, Italy, MA in Economics +- 1993-1997 Università degli Studi di Torino, Italy, Laurea in Economia, Summa cum Laude + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- H. T. Warshow and Robert Irving Warshow Professor of Economics and Professor of Statistics, +- Cornell University, 2017 – present. +- Professor of Economics and Professor of Statistics, Cornell - University, 2014–2016. +- Associate Chair, Department of Economics, Cornell University, 2012–2013. +- Associate Professor of Statistics, Cornell University, 2013. +- Associate Professor of Economics (with tenure), Cornell University, 2009–2013. +- Assistant Professor of Economics, Cornell University, 2003–2009. + +# Work experience[中文] + +# Publication[English] +- **Random Sets in Econometrics, with Ilya Molchanov**, Econometric Society Monograph Series, Cambridge University Press, 2018. +- **Constraint Qualifications in Partial Identification**, with Hiroaki Kaido and Joerg Stoye, April 2021, forthcoming in Econometric Theory. +- **Precise or Imprecise Probabilities? Evidence from survey response related to late-onset dementia**, with Pamela Giustinelli and Chuck Manski, March 2021. Accepted for publication in the Journal of the European Economic Association. +- **Heterogeneous Choice Sets and Preferences**, with Levon Barseghyan, Maura Coughlin, and Joshua C. Teitelbaum, February 2021. Forthcoming in Econometrica. +- **Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study**, with Pamela Giustinelli and Chuck Manski, with Online Appendix, September 2020, forthcoming in the Journal of Econometrics. +- **Discrete Choice Under Risk with Limited Consideration**, with Levon Barseghyan and Matt Thirkettle, American Economic Review, vol. 111, pp. 1972-2006, June 2021. +- **Local Regression Smoothers with Set-Valued Outcome Data**, with Qiyu Li, Ilya Molchanov and Sida Peng, International Journal of Approximate Reasoning, Volume 128, Pages 129-150, January 2021. +- **Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem**, with Chuck Manski, Journal of Econometrics, vol. 220, pp. 181-192, January 2021. +- **Microeconometrics** with Partial Identification, Handbook of Econometrics, Volume 7, Part A, pp. 355-486, 2020. +- **The Cost of Legal Restrictions on Experience Rating**, with Levon Barseghyan, Darcy Steeg Morris, and Joshua C. Teitelbaum, Journal of Empirical Legal Studies, Volume 17, Issue 1, Pages 38-70, March 2020. +- **Confidence Intervals for Projections of Partially Identified Parameters**, with Hiroaki Kaido and Joerg Stoye, Econometrica, vol. 87, pp. 1397-1432, July 2019. +- **Estimating Risk Preferences in the Field**, with L. Barseghyan, T. O'Donoghue and Josh Teitelbaum, Journal of Economic Literature, vol. 56, pp. 501-564, July 2018. +- **Inference Under Stability of Risk Preferences**, with Levon Barseghyan, and Josh Teitelbaum, Quantitative Economics, vol. 7, pp. 367-409, July 2016. +- **Applications of Random Set Theory in Econometrics**, with Ilya Molchanov, Annual Review of Economics, Volume 6, pages 229-251, August 2014. +- **The Nature of Risk Preferences: Evidence from Insurance Choice**, with Levon Barseghyan, Ted O'Donoghue and Josh Teitelbaum, American Economic Review, Volume 103, Number 6, Pages 2499-2529, October 2013. +- **Distinguishing Probability Weighting from Risk Misperceptions in Field Data**, with Levon Barseghyan, Ted O'Donoghue and Josh Teitelbaum, American Economic Review Papers and Proceedings, 2013, vol. 103, number 3, 580-585. +- **Partial Identification Using Random Set Theory**, with Arie Beresteanu and Ilya Molchanov, Journal of Econometrics, 2012, vol. 166, issue 1, pages 17-32. ERRATA. +- **Sharp Identification Regions in Models with Convex Moment Predictions**, with Arie Beresteanu and Ilya Molchanov, Econometrica, 2011, vol. 79, issue 6, pages 1785-1821; with Online Supplement. +- **Rounding Probabilistic Expectations in Surveys**, with Chuck Manski, Journal of Business and Economic Statistics, 2010, vol. 28, number 2, pages 219-231. +- **Missing Treatments**, Journal of Business and Economic Statistics, 2010, vol. 28, number 1, pages 82-95. +- **The Identification Power of Equilibrium in Games: The Supermodular Case**, with Adam Rosen, Journal of Business and Economic Statistics, 2008, vol. 26, number 3, pages 297-302. Invited discussion of Aradillas-Lopez and Tamer (2008) prepared for the 2007 Joint Statistical Meetings. +- **Asymptotic Properties for a Class of Partially Identified Models**, with Arie Beresteanu, Econometrica, 2008, vol. 76, issue 4, pages 763-814. +- **Partial Identification of Probability Distributions** with Misclassified Data, Journal of Econometrics, 2008, vol. 144, issue 1, pages 81-117. +- **Skip Sequencing: A Decision Problem in Questionnaire Design**, with Chuck Manski, Annals of Applied Statistics, 2008, vol. 2, number 1, 264-285. +- **Spatial Correlation Robust Inference with Errors in Location and Distance**, with Timothy G. Conley, Journal of Econometrics, 2007, vol. 140, issue 1, pages 76-96. Download the Technical Appendix here. +- **Generalization of a Result on 'Regressions: Short and Long'**, with Marcin Peski, Econometric Theory, 2006, vol. 22, issue 01, pages 159-163. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Giles-Hooker-.md b/Data/CU-DSS/CU-DSS-Giles-Hooker-.md new file mode 100644 index 0000000..eefe6d1 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Giles-Hooker-.md @@ -0,0 +1,268 @@ +--- +bio-current: + name-cn: + name_en: Giles Hooker + email: + - gjh27@cornell.edu + - ghooker@berkeley.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - Department of Computational Biology[https://cb.cornell.edu/] + major: + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://faculty.bscb.cornell.edu/~hooker/ + - https://statistics.berkeley.edu/people/giles-hooker + - https://research.cornell.edu/researchers/giles-hooker + github: + googlescholar: + - https://scholar.google.com/citations?user=iZgkmFIAAAAJ + aminer: + - https://www.aminer.cn/profile/giles-hooker/53f43295dabfaedce550795f + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Stanford University + school: + email: + date-start: + date-end: 2004 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: Stanford University + school: + date-start: + date-end: 2002 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Australian National University + school: + major: + date-start: + date-end: + - BSc 1999 + - BA 1998 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - http://faculty.bscb.cornell.edu/~hooker/gilesh/gilesh.pdf + research: + - http://faculty.bscb.cornell.edu/~hooker/gilesh/gilesh.pdf + software: + - http://faculty.bscb.cornell.edu/~hooker/gilesh/gilesh.pdf + project: + blog: + - http://blogs.cornell.edu/modelmeanings/ + arxiv: + linkedin: + - https://uk.linkedin.com/in/giles-hooker-b7703828 + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Giles Hooker ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Giles%20Hooker_crop.jpg?itok=_ns9SEBF) + +# Biography[English] +I have a number of research areas and active projects. For a complete description, my CV and links to publications, please see my website at the Department of Computational Biology. + +As a brief description, I am interested in + +**Machine Learning**: In particular, I focus on the use of heuristic predictive algorithms within more classical statistical methods. Many successful methods in machine learning produce "black box" models that make accurate predictions but cannot be readily interpreted. I am interested in using these methods to understand questions like "Which variables are important in this prediction?" and "How do these two variables to combine to produce this prediction?" I want to both give answers to these questions and to provide measures of the strength of evidence behind these answers. + +I am also interested in using these methods within statistical models; using them to predict things that we can only indirectly measure. As an example, in my work with the Laboratory of Ornithology we would like to predict bird migration, but can only observe where birds are, not their migratory movement. + +**Nonlinear Dynamics**: While statistical models are generally built to explain observations and involve linear models or variants of them, much of applied mathematics has been developed through first-principles modelling, producing dynamic systems described by ordinary differential equations along with more complex models. I am interested in developing statistical techniques to interface these dynamical models with data. The problems in this field include parameter estimation, providing confidence intervals and tests of parameters, assessing goodness of fit and the means for improving it and designing experiments for systems that are governed by dynamic systems. + +**Robust Statistics**: I work on a class of models called disparity estimates. These involve first estimating a non-parametric version of the model and then comparing this estimate to a parametric description. The extra smoothness that you gain from the non-parametric estimate allows you to use a comparison metric -- Hellinger distance is the best know of these -- that makes your parameter estimates insensitive to outlying data points without giving up statistical precision. These proceedures can be readily examined in simple cases (univariate, i.i.d. data); my research aims to extend these methods to models that commonly used; regression, generalized linear models, time series, random effects models etc. + +**Functional Data Analysis**: Functional Data describe high-resolution measurements of repeated processes: think motion capture data of several people performing the same activity (walking, reaching, writing...) or of one person repeating it numerous times. There are many other areas of application and I work on satelite imaging data and vehicular emissions as particular examps. I maintain the fda library in R providing tools to analyze such data. I also develop new methods including for latent functional data, functional data that is measured over spatial domains and regression models to predict an outcome from functional covariates. + +**Consulting and Other Applications**: In addition to the areas above, part of my job involves statistical consulting to the Cornell community and I have been involved in a wide range of applied problems. I have also worked on Item Response Theory and its applications to analyzing web browsing behavior, educational testing and medical diagnostics. + +# Biography[中文] + +# Interests[English] +- Machine Learning +- Nonlinear Dynamics +- Robust Statistics +- Functional Data Analysis +- Consulting and Other Applications + +# Interests[中文] + +# Education[English] +- PhD Statistics, Stanford University 2004. +- MSc Statistics, Stanford University, 2002. +- BSc Mathematics Honours 1, Australian National University, 1999. +- BA Political Science, Australian National University, 1998. + +# Education[中文] + +# Awards[English] +- 2011 NSF CAREER Award +- 2004 TA of the Year, Department of Statistics, Stanford University. +- 2004 Laha Travel Award, Institute of Mathematical Statistics +- 2003 TA Award - Department of Statistics, Stanford University. +- 2000 Fulbright Tim Matthews Memorial Award +- 2000 Shandong Province Award for Excellence in Teaching +- 1999 University Medal in Mathematics, Australian National University +- 1999 Hana Neumann Prize, Australian National University +- 1999 Australian National University Honours Scholarship +- 1997 University of New South Wales Summer Research Scholarship + +# Awards[中文] + +# Talks[English] +- “Optimal Adaptive Design of Experiments for Stochastic Dynamic Systems”, Statistics Seminar, University of Pennsylvania, November 2020. +- “Ensembles of Trees and CLT’s: Inference and Machine Learning”, Statistics Seminar, Johns Hopkins University, September 2020. +- “Statistics and Dynamical Systems”, Introductory Overview Lecture with Sy-Miin Chow, Joint Statistical Meetings, August 2020. +- “Ensembles of Trees and CLT’s: Inference and Machine Learning”, Statistics Seminar, Australian National University, May 2020. +- “Ensembles of Trees and CLT’s: Inference and Machine Learning”, Statistics Seminar, Monash University, March 2020. +- “Ensembles of Trees and CLT’s: Inference and Machine Learning”, Statistics Seminar, University of Melbourne, March 2020. +- “Optimal Adaptive Design of Experiments for Stochastic Dynamic Systems”, Neyman Seminar, University of California at Berkeley, January 2020. +- “Ensembles of Trees and Inference Boosting, U and V Statistics”, Statistics Seminar, Temple University, November 2019. +- “Ensembles of Trees and CLT’s: Inference and Machine Learning”, Neyman Seminar, University of California at Berkeley, November 2019. +- “Inference and Gradient Boosting”, Conference of the Israel Statistics Association, Tel Aviv,June 2019. +- “Subsample Trees and CLTs: Inference for Machine Learning”, Statistics Seminar, Tel Aviv University, June 2019. +- “Optimal Adaptive Design of Experiments for Stochastic Dynamic Systems”, Statistics Seminar, Ben Gurion University, June 2019. +- “Inference and Gradient Boosting”, Workshop in Celebration of Jerry Friedman’s 80th birthday, May, 2019. +- “Boosting and Bagging and Uncertainty Quantification”, Statistics Seminar, University of Pennsylvania, April, 2019. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, Iowa State University, October 2018. +- “Explanations, Interpretation, Uncertainty Quantification”, Machine Learning in Medicine Symposium, Cornell University, September 2018. +- “An ODE to Statistics: Inference about Nonlinear Dynamics”, PIMS-IAM Distinguished Colloquium, University of British Columbia, January 2018. +- “Testing Curvature in Functional Single Index Models: Inference for Ecological Reaction Norms”, Joint Statistical Meetings, August 2017. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, Imperial College London, May 2017. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, Nottingham University, May 2017. +- “Decision Trees and CLT’s: Machine Learning and Ecological Inference”, Ecology Seminar,Yale University, April 2017. +- “Tests for Lack of Fit and Missing State Variables in Ordinary Differential Equation Models”,Statistics Seminar, University of Cyprus, April 2017. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, University +of Cyprus, February 2017. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, Simon Fraser University, November 2016. +- “Truncated Linear Models for Functional Data”, Royal Statistical Society Meetings, JRSSB Editors Invited Session, September 2016. +- “On the Range of Integration of a Functional Linear Model”, CRoNoS Workshop on Functional Data Analysis, August 2016. +- “Testing High Dimensional Interactions with Random Forests”, COMPSTAT, August 2016. +- “Testing High Dimensional Interactions with Random Forests”, International Chinese Statistics Symposium, July 2016. +- “Three Unidentfiable Problems in Functional Data Analysis”, Workshop on Functional Data Analysis, Les Diablerets, May 2016. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, Temple University, February 2016. +- “Tests for Lack of Fit and Missing State Variables in Ordinary Differential Equation Models”,York University, October 2015. +- “Tests for Lack of Fit and Missing State Variables in Ordinary Differential Equation Models”,University of Michigan, September 2015. +- “Modeling Covariance in Functional Data Analysis”, Joint Statistical Meetings, August 2015. +- “An ODE to Statistics: Inference for Nonlinear Dynamics”, Statistics Seminar, Syracuse University, January 2015. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Biostatistics Seminar, Weil Cornell Medical College, January 2015. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Statistics Seminar, North Carolina State University, October 2014. +- “Subsample Trees and CLTs: Inference for Machine Learining”, Artificial Intelligence Seminar, Cornell University, October 2014. +- “Robustness, Inference and Gradient Matching”, BIRS Workshop on Statistics and Nonlinear Dynamics in Biology and Medicine, July 2014. +- “Domain Selection and Truncated Functional Linear Models”, IMS Meetings, July 2014. +- “Experimental Design in Stochastic Dynamic Systems”, Mathematics and Business Analytics Seminar, IBM Research, June 2014. +- “Ensemble Trees and CLTs: Inference for Machine Learning”, Health Care Analytics Seminar,IBM Research, April 2014. +- “Experimental Design in Stochastic Dynamic Systems”, Statistics Seminar, University of British Columbia, January 2014. +- “Tests for Lack of Fit and Missing State Variables in Ordinary Differential Equation Models”,Statistics Seminar, Simon Fraser University, January 2014. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, Summer Camp, Australian National University, December 2013. +- “Paradoxical Results in Multidimensional Item Response Theory”, Applied Statistics Seminar,Australian National University, November 2013. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, Statistics Seminar, University of Melbourne, October 2013. +- “Control Theory and the Design of Dynamic Experiments”, Statistics Seminar, University of California at Davis, 2013. +- “Measures of Robustness in Regularized Estimates”, Joint Statistical Meetings, August 2013. +- “Measures of Robustness in Regularized Estimates”, International Conference on Robust Statistics, July 2013. +- “Functional Convolution Models: Design and Domain Selection”, Statistical Society of Canada Meetings, June 2013. +- “Control Theory and the Design of Dynamic Experiments”, Mathematics Seminar, De Paul University, April 2013. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, Statistics Seminar, University of Wisconsin, Madison, April 2013. +- “Control Theory and the Design of Dynamic Experiments”, Mathematics Seminar, Karlsruhe Institute fur Technologie, January 2013. ¨ +- “Control Theory and the Design of Dynamic Experiments”, Greek Stochastics Meetings, August, 2012. +- “Control Theory and the Design of Dynamic Experiments”, Statistical Society of Canada Meetings, June 2012. +- “Detecting Evolution in Experimental Ecology: Diagnostics for Missing State Variables”,Statistics Seminar, Cornell University, March 2012. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, Statistics Seminar, University of Waterloo, January 2012. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, Statistics Seminar, University of Chicago, January 2012. +- “Detecting Evolution in Experimental Ecology: Diagnostics for Missing State Variables”,Ecology and Evolution Seminar, University of Chicago, January 2012. +- “Detecting Evolution in Experimental Ecology: Diagnostics for Missing State Variables”, Biostatistics Seminar, Johns Hopkins University, January 2012. +- “Bayesian Robustness via Disparities”, International Conference on Advances in Probability and Statistics, Hong Kong, December 2011. +- “Robustness, Efficiency and Regularization in Model Selection: LASSO with Disparities”,Conference on Statistical Concepts and Methods for the Modern World, Colombo, Sri Lanka,December 2011. +- “Robustness and Efficiency in Conditionally Specified Models via Disparities”, 2011 International Forum on Modern Statistics and Econometrics, Wang Yanan Institute for Studies inEconomics, Xiamen, China, December 2011. +- “Detecting Evolution in Experimental Ecology: Diagnostics for Missing State Variables”, Applied Mathematics Seminar, University of Colorado, December 2011. +- “Detecting Evolution in Experimental Ecology: Diagnostics for Missing State Variables”, Colloque de Statistique de Montreal, Centre de Recherces Math ´ ematiques, Montreal, December ´2011. +- “Robust and Efficient Analysis of Conditionally-Specified Models via Disparities”, University of Michigan, September 2011. +- “Functional Convolution Models, Design and Domain Selection”, The University of Melbourne, June 2011. +- “Robust and Efficient Analysis of Non-Stationary Data via Disparities”, Macquarie University,June 2011. +- “Functional Convolution Models”, ENAR, March 2011. + +# Talks[中文] + +# Work experience[English] +- March 2019 - Associate Professor, Department of Statistics and Data Science and Department of Computational Biology, Cornell University. +- July 2016 - December 2019 Director of Undergraduate Studies, Biometry and Statistics,Major, Cornell University +- May 2012 - March 2019 Associate Professor, Department of Biological Statistics and Computational Biology and Department of Statistical Science, Cornell University. +- June 2012 - June 2016 Director of Graduate Studies, Graduate Field of Statistics, Cornell University. +- June 2012 - September 2013 Director of Graduate Studies, Graduate Field of Biometry,Cornell University. +- August 2006 - May 2012 Assistant Professor, Department of Biological Statistics and Computational Biology and Department of Statistical Science, Cornell University. +- October 2004- August 2006 Post-Doctoral Fellow, McGill University. +- June - September 2003 Intern, Robert Bosch Corp. +- June 2002 - August 2002 Intern, AT&T Research. +- Feb-July 2000 English Teacher, Chiang Chen Industrial Institute. +- Dec 1999 - Feb 2000 Research Assistant, Research School of Information Sciences and Engineering. +- Dec 1998 - Feb 1999 Intern, Commonwealth Bank of Australia. +- Dec 1997 - Feb 1998 Research Scholar, University of New South Wales. + +# Work experience[中文] + +# Publication[English] +- Ramsay, James O. and **Giles Hooker**, 2017, **“Dynamic Data Analysis: Modeling Data with Differential Equations”**, Springer. +- Ramsay, James O., **Giles Hooker** and Spencer Graves, 2009, **“Functional Data Analysis with R and Matlab”**, Springer. +- Ye, Zi, **Giles Hooker** and Stephen P. Ellner, 2021, **“Generalized Single Index Models andJensen Effects on Reproduction and Survival”**, Journal of Agricultural, Biological, and Environmental Statistics, in press. +- Tredennick, Andrew T., **Giles Hooker**, Stephen P. Ellner and Peter B. Adler, 2020, **“A practical guide to selecting models for exploration, inference, and prediction in ecology”**, Ecology, +in press. +- Warmenhoven, John, Norma Bargary, Dominik Liebl, Andrew Harrison, Mark Robinson,Edward Gunning and **Giles Hooker**, 2020, **“PCA of Waveforms and Functional PCA: A Primer for Biomechanics”**, Journal of Biomechanics, in press. +- Zhou, Zhengze, and **Giles Hooker**, 2020, **“Unbiased Measurement of Feature Importance in Tree-Based Methods”**, Transactions in Knowledge Discovery and Data Mining, in press. +- Snyder, Robin, Stephen P. Ellner and **Giles Hooker**, 2020, **“Time and chance: using age partitioning to understand how luck drives variation in reproductive success”**, The American +Naturalist, in press. +- Ghosal, Indrayudh and **Giles Hooker**, 2020, **“Boosting Random Forests to Reduce Bias;One-Step Boosted Forest and its Variance Estimate”**, Journal of Computational and Graphical Statistics, in press. +- Ye, Zi and **Giles Hooker**, 2020, **“Local Quadratic Estimation of the Curvature in a Functional Single Index Model”**, Scandinavian Journal of Statistics, 47(4)1307-1338. +- Ye, Zi, **Giles Hooker** and Stephen P. Ellner, 2020, **“The Jensen Effect and Functional Single Index Models: Estimating the Ecological Implications of Nonlinear Reaction Norms”**, Annals +of Applied Statistics, 14(3):1326-12341. +- Coleman, Tim, Lucas Mentch, Daniel Fink, Frank La Sort, **Giles Hooker**, Wesley Hochachka and David Winkler, 2020, **“Statistical Inference on Tree Swallow Migrations”**, Journal of the +Royal Statistical Society, Series C, in press. +- **Hooker, Giles**, Sophia Brumer, Christopher Zappa and Edward Monahan, 2020, **“Inferences to be Drawn from a Consideration of Power-Law Descriptions of Multiple Data Sets Each Comprised of Whitecap Coverage, WB, and 10-m Elevation Wind Speed Measurements”**, inRecent Advances in the Study of Oceanic Whitecaps, P. Vlahos and E. C. Monahan (Eds). +- Kilian, Nicole, Yongden Zhang, Lauren LoMonica, **Giles Hooker**, Derek Toomre, Choukri Ben Mamoun and Andreas. M. Ernst, 2020, **“Trafficking and Localization of S-Palmitoylated Proteins in Plasmodium falciparum-Infected Erythrocytes”**, BioEssays, 1900145. +- Javeed, Aurya, and **Giles Hooker**, 2020, **“Timing Observations of Diffusions”**, Statistics and Computing, 30:405-417. +- J. Wen, P. Kohler, G. Duveiller, N.C. Parazoo, T.S. Magney, ¨ **G. Hooker**, L. Yu, C. Y. Chang,and Y. Sun, 2020 **“Generating a Long-Term Record of High-Resolution Global Solar-Induced Chlorophyll Fluorescence (SIF) by Harmonizing Multiple Satellite Instruments: A Case Study for Fusing GOME-2 and SCIAMACHY”**, Remote Sensing of Environment, in press. +- Sinclair, David G., and **Giles Hooker**, 2019, **“Sparse Inverse Covariance Estimation for Highthroughput microRNA Sequencing Data in the Poisson Log-Normal Graphical Model”**, Journal of Statistical Computation and Simulation, in press. +- Ellner, Stephen P., Snyder, Robin E., Adler, Peter B. and **Giles Hooker**, 2019, **“An Expanded Modern Coexistence Theory for Empirical Applications”**, Ecology Letters, 22(1):3-18. +- Goryaynov, Alexander, Nicole Kilian, Mark Lessard, Derek Toomre, James Rothman, **Giles Hooker** and Jorg Bewersdorf, 2018 **“Assessing photodamage in live-cell STED microscopy”**, ¨ +Nature Methods, 15:755-756. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-James-Booth-.md b/Data/CU-DSS/CU-DSS-James-Booth-.md new file mode 100644 index 0000000..778d903 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-James-Booth-.md @@ -0,0 +1,212 @@ +--- +bio-current: + name-cn: + name_en: James Booth + email: + - jim.booth@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Data Science + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://faculty.bscb.cornell.edu/~booth/ + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/james-booth/560288b645cedb3395fcb915 + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Kentucky + school: + email: + date-start: + date-end: 1987 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + - University of Kentucky + - University of Leeds + school: + date-start: + date-end: + - MS 1984 + - MSc 1982 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: University of Leeds + school: + major: Mathematics + date-start: + date-end: 1981 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - http://faculty.bscb.cornell.edu/~booth/cv-mar2021.pdf + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![James Booth ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Jim%20Booth_crop.jpg?itok=3hqwdDQ-) + +# Biography[English] +I am currently a Professor in the Department of Statistics and Data Science at Cornell University, one of three departments in Computing and Information Science. I visited the Department of Operations Research and Information Engineering at Cornell in 2003, and was hired in the Department of Biological Statistics and Computational Biology, in the College of Agricultural and Life Sciences, the following year. From 1987 to 2003 I was a faculty member in the Department of Statistics at the University of Florida. During that period I spent two years as a Research Fellow at the Australian National University, and one year at Colorado State University. My research interests involve basic statistical methodology including: the bootstrap and Monte Carlo methods, clustering, exact inference, mixed models, generalized linear models, and also applications in bioinformatics. I have taught a variety of courses at Cornell including Statistical Methods II, the second semester of a statistical methods sequence for graduate students from a wide variety of disciplines, Biological Statistics I, Data Science for All, as well as core courses for statistics undergraduates, professional masters students, and Ph.D. students in the Fields of Statistics. As a CALS faculty member in SDS part of my teaching effort involves contributions to the campus-wide statistical consulting service through the Cornell Statistical Consulting Unit. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] +PhD Statistics 1987 University of Kentucky +MS Statistics 1984 University of Kentucky +MSc Statistics 1982 University of Leeds, England +BSc Mathematics 1981 University of Leeds, England + +# Education[中文] + +# Awards[English] + +# Awards[中文] +- Fellow of the American Statistical Association: 2004 +- Best Contributed Presentation: Joint Statistical Meetings, New York, 2003 +- Teaching Improvement Program Award: University of Florida, 1995 +- R.L. Anderson Outstanding Graduate Student: University of Kentucky, 1985 + + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Professor 2004 – present Cornell University +- Visiting Professor 2017 – Spring Australian National University +- Visiting Professor 2016 – Fall University of Connecticut +- Visiting Professor 2003 – 2004 Cornell University +- Professor 2000 – 2004 University of Florida +- Visiting Professor 1996 – 1997 Colorado State University +- Associate Professor 1993 – 2000 University of Florida +- Research Fellow 1990 – 1992 Australian National University +- Assistant Professor 1987 – 1993 University of Florida +- Instructor 1986 – 1987 Transylvania University, KY + +# Work experience[中文] + +# Publication[English] +- Gaynanova, I., **Booth, J. G.** & Wells, M. T. (2016). **Simultaneous sparse estimation of canonical vectors in the p>>n setting.** Journal of the American Statistical Association 111, 696–706. Published online 16 Apr 2015. +- Bar, **Booth**, Wells (2014), **"A bivariate model for simultaneous testing in bioinformatics data"**, Journal of the American Statistical Association 109(506):537-547. +- Kormaksson, **Booth**, Figueroa and Melnick (2012) **"Integrative model-based clustering of microarray methylation and expression data"**. Annals of Applied Statistics 6(3):1327-1347. +- Bar, **Booth**, Schifano and Wells (2010), **"Laplace approximated EM Microarray Analysis: an empirical Bayes approach for comparative microarray experiments"**. Statistical Science 25(3):388-407. Please contact Haim Bar at the University of Connecticutt with questions about the Lemma software package. +- **Booth**, Federer, Wells and Wolfinger (2009), **"A multivariate variance component model for analysis of covariance in designed experiments"**, Statistical Science 24(2):223-237. +- Sarah E. Lyons, Zhehan Tang, **James Booth**, Quirine M. Ketterings (2018), **"Nitrogen Response Models for Winter Cereals Grown for Forage"**, Journal of Agronomy and Crop Science. +- Salerno, S., Mehrmohamadi, M., Liberti, M.V., Wan, M., Wells, M.T., **Booth, J.G.** and Locasale, J.W., (2017), **"RRmix: A method for simultaneous batch effect correction analysis of metabolomics data in the absence of internal standards"**, PLoS ONE. +- Love, W., Zawack, K., **Booth, J.G.**, Grohn, Y.T and Lanzas, C. (2016), **"Markov networks of collateral resistance: National antimicrobial resistance monitoring system surveillance results from Escherichia coli Isolates, 2004-2012"**, PLoS Computational Biology, 12: e1005160. +- Eilertson, **Booth**, Bustamante (2012) **"SnIPRE: Selection inference using a Poisson random effects model"** PLoS Computational Biology 8(12): e1002806. +- Hirschl, **Booth**, Glenna and Green (2012), **"Politics, religion, and society: Is the United States experiencing a period of religious-political polarization?"**, Review of European Studies 4(4) +- Haim Y. Bar, **James G. Booth**, and Martin T. Wells. **Mixed effects modeling and variable selection for quantile regression.** Statistical Modelling, 2021. Invited revision. +- **James G. Booth** and Alan H. Welsh. **Generalized regression estimation via the bootstrap.**Australian and New Zealand Journal of Statistics, 62(1):5–24, 2020. +- Haim Bar, **James G. Booth**, and Martin T. Wells. **A scalable empirical bayes approach to variable selection in generalized linear models.** Journal of Computational and Graphical Statistics, 29(3):535–546, 2020. +- Xiaoyun Quan, **James G. Booth**, and Martin T. Wells. **Rank-based approach for estimating correlations in mixed ordinal data.** Journal of Multivariate Analysis, 2019. Under review. +- Muting Wan, **James G. Booth**, and Martin T. Wells. **Variational bayes for hierarchical mixture models.** In Wolfgang Hardle, Henry Lu, and Xiaotong Shen, editors, **Handbook of Big Data Analytics**, chapter 7, pages 151–201. Springer International Publishing, 2018. +- Haim Bar, **James G. Booth**, Kangyan Liu, and Martin T. Wells. **Facilitating high-dimensional transparent classificiation via empirical Bayes variable selection.** Applied Stochastics Models in Business and Industry, 2018. +- Irina Gaynanova, **James G. Booth**, and Martin T. Wells. **Penalized versus constrained generalized eigenvalue problems.** Journal of Computational and Graphical Statistics, 26(2):379–387,2017. Posted online 06 Apr 2016. +- Irina Gaynanova, **James G. Booth**, and Martin T. Wells. **Simultaneous sparse estimation of canonical vectors in the p>>n setting.** Journal of the American Statistical Association,111(514):696–706, 2016. Published online 16 Apr 2015. +- Haim Y. Bar, **James G. Booth**, and Martin T. Wells. **A bivariate model for simulataneous testing in bioinformatics data.** Journal of the American Statistical Association, 109(506):537–547, 2014. +- Matthias Kormaksson, **James G. Booth**, Maria E. Figueroa, and Ari Melnick. **Integrative model-based clustering of microarray methylation and expression data.** Annals of Applied Statistics, 6(3):1327–1347, 2012. +- Haim Y. Bar, **James G. Booth**, and Martin T. Wells. **A mixture-model approach for parallel testing for unequal variances.** Statistical Applications in Genetics and Molecular Biology, 11(1),2012. Article 8. +- Raazesh Sainudiin, Keven Thornton, Jennifer Harlow, **James Booth**, Michael Stillman,Ruriko Yoshida, Robert Griffiths, Gil McVean, and Peter Donelly. **Experiments withthe site frequency spectrum.** Bulletin of Mathematical Biology, 73(4):829–872, 2011. +- Caitlin Cunningham and **James G. Booth**. **A Bayesian approach to analysis of covariance in balanced randomized block experiments.** Journal of Statistical Computation and Simulation,81(11):1449–1460, 2011. +- Haim Bar, **James Booth**, Elizabeth Schifano, and Martin T. Wells. **Laplace approximated EM microarray analysis: an empirical bayes approach for comparative microarray experiments.**Statistical Science, 25(3):388–407, 2010. +- **James G. Booth**, Walter T. Federer, Martin T. Wells, and Russell Wolfinger. **A multivariate variance component model for analysis of covariance in designed experiments.** Statistical Science, 24(2):223–237, 2009. +- Vadim Zipunnikov, **James G. Booth**, and Ruriko Yoshida. **Table counting using the saddlepoint approximation.** Journal of Computational and Graphical Statistics, 18(4):915–929, 2009. +- **James G Booth**, George Casella, and James P Hobert. **Clustering using objective functions and stochastic search.** Journal of the Royal Statistical Society, B 70(1):119–139, 2008. +- Yongsung Joo, **James G. Booth**, Younghwan Namkoong, and George Casella. Model-based Bayesian clustering (MBBC). Bioinformatics, 24:874–875, 2008. +- Ronald W. Butler, Richard K. Sutton, **James G. Booth**, and Pamela Ohman Strickland. **Simulation-assisted saddlepoint approximation.** Journal of Statistical Computation and Simulation, 79(8):731–745, 2008. +- David B. Hitchcock, **James G. Booth**, and George Casella. **The effect of pre-smoothing functional data on cluster analysis.** Journal of Statistical Computation and Simulation, 77(12):1043–1055, 2007. +- Yongsung Joo, George Casella, **James G. Booth**, Keunbaik Lee, and Steven Enkeman. Normalization of dye bias in microarrays using the mixture of splines model. Statistical Applications in Genetics and Molecular Biology, 6(1), 2007. Art. 2. +- David B. Hitchcock, George Casella, and **James G. Booth**. **Improved estimation of dissimilarities by smoothing functional data.** Journal of the American Statistical Association,101(473):211–222, 2006. +- **James G. Booth**, Marinela Capanu, and Ludwig Heigenhauser. **Exact conditional p-value calculation for the quasi-symmetry model.** Journal of Computational and Graphical Statistics,14(3):716–725, 2005. +- **James G. Booth**, George Casella, Herwig Friedl, and James P. Hobert. **Negative binomial loglinear mixed models.** Statistical Modelling, 3(3):179–191, 2003. +- Glen L. Hartless, **James G. Booth**, and Ramon C. **Littell. Local influence of predictors in multiple linear regression.** Technometrics, 45(4):326–332, 2003. +- Wolfgang Jank and **James G. Booth**. Efficiency of Monte Carlo EM and simulated maximum likelihood in generalized linear mixed models. Journal of Computational and Graphical Statistics, 12(1):214–229, 2003. +- Brian S. Caffo and **James G. Booth**. **Monte Carlo conditional tests for log-linear and logistic models: a survey of current methodology.** Statistical Methods in Medical Research, 12:1–15,2003. +- Brian S. Caffo, **James G. Booth**, and Anthony C. Davison. **Empirical sup rejection sampling.** Biometrika, 89:745–754, 2002. +- **James G. Booth** and Brian S. Caffo. **Unequal sampling for Monte Carlo EM algorithms.** Computational Statistics and Data Analysis, 39:261–270, 2002. +- **James G. Booth**, James P. Hobert, and Wolfgang Jank. **A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model.** Statistical Modelling, 1:333–349, +2001. +- Brian S. Caffo and **James G. Booth**. **A Markov chain Monte Carlo algorithm for approximating exact conditional tests.** Journal of Computational and Graphical Statistics, 10:730–745, 2001. +- Alan Agresti, **James G. Booth**, James P. Hobert, and Brian Caffo. **Random effects modeling of categorical response data.** Sociological Methodology, 30:27–80, 2000. +- **James G. Booth** and James P. Hobert. **Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm.** Journal of the Royal Statistical Society, B +61:265–285, 1999. +- **James G. Booth**, James P. Hobert, and Pamela A. Ohman. **On the probable error of the ratio of two gamma means.** Biometrika, 86:439–452, 1999. +- **James G. Booth** and Ronald W. Butler. **An importance sampling algorithm for exact conditional tests in log-linear models.** Biometrika, 86:321–332, 1999. +- **James G. Booth** and James P. Hobert. **Standard errors of prediction in generalized linear mixed models.** Journal of the American Statistical Association, 93:262–272, 1998. +- **James G. Booth** and Somnath Sarkar. **Monte Carlo approximation of bootstrap variances.** American Statistician, 52:354–357, 1998. +- **James G. Booth** and Brett Presnell. **Allocation of Monte Carlo resources for the iterated bootstrap.** Journal of Computational and Graphical Statistics, 7:92–112, 1998. +- **James G. Booth**, Ronald W. Butler, Snehelata Huzurbazar, and Andrew T. A. Wood. **Saddlepoint approximations for p-values of some tests of covariance matrices.** Journal of StatisticalComputation and Simulation, 53:165–180, 1995. +- **James G. Booth** and Andrew T. A. Wood. **An example in which the lugannani-rice saddlepoint formula fails.** Statistics and Probability Letters, 23:53–61, 1995. +- **James G. Booth**, Ronald W. Butler, and Peter Hall. **Bootstrap methods for finite populations.** Journal of the American Statistical Association, 89:1282–1289, 1994. +- **James G. Booth**, Peter Hall, and Andrew T. A. Wood. **On the validity of edgeworth and saddlepoint expansions.** Journal of Multivariate Analysis, 51:121–138, 1994. +- **James G. Booth** and Peter Hall. **Monte Carlo approximation and the iterated bootstrap.** Biometrika, 81(2):331–340, 1994. +- **James G. Booth** and Peter Hall. **An improvement of the jackknife distribution function estimator.** Annals of Statistics, 21(3):1476–1485, 1993. +- **James G. Booth** and Peter Hall. **Bootstrap confidence regions for functional relationships in errors-in-variables models.** Annals of Statistics, 21(4):1780–1791, 1993. +- **James G. Booth** and Kim-Anh Do. **Simple and efficient resampling methods for constructing bootstrap confidence intervals.** Computational Statistics, 8:333–346, 1993. +- Andrew T. A. Wood, **James G. Booth**, and Ronald W. **Butler. Saddlepoint approximations to the cdf of some statistics with nonnormal limit distributions.** Journal of the AmericanStatistical Association, 88(422):680–686, 1993. +- **James G. Booth**, Peter Hall, and Andrew T. A. Wood. **Balanced importance resampling for the bootstrap.** Annals of Statistics, 21(1):286–298, 1993. +- Ronald W. Butler, **James G. Booth**, and Snehelata Huzurbazar. **Saddlepoint approximations for tests of block independence, sphericity, and equal variances and covariances.** Journal of the +Royal Statistics Society, B 55:171–184, 1993. +- Ronald W. Butler, Snehelata Huzurbazar, and **James G. Booth**. **Saddlepoint approximations for the bartlett–nanda–pillai trace statistic in multivariate analysis.** Biometrika, 79:705–715,1992. +- **James G. Booth**. **A note on a one compartment model with clustering.** Journal of Applied +Probability, 29:535–542, 1992. +- **James G. Booth**, Peter Hall, and Andrew T. A. Wood. **Bootstrap estimation of conditional distributions.** Annals of Statistics, 20(3):1594–1610, 1992. +- Ronald W. Butler, Snehelata Huzurbazar, and **James G. Booth**. **Saddlepoint approximations to the generalized variance and wilk’s statistic.** Biometrika, 79:157–169, 1992. +- **James G. Booth**. **A note on the estimation of rate parameters in stochastic compartmental models**. Communications in Statistics, B 20(1):391–397, 1991. +- **James G. Booth** and Ronald W. Butler. **Randomization distributions and saddlepoint approximations in generalized linear models.** Biometrika, 77:787–796, 1990. +- **James G. Booth**. **On the limiting behavior of downton’s carrier epidemic in the case of a general infection mechanism**. Journal of Applied Probability, 26:625–630, 1989. +- **James G. Booth**, Joseph Gani, Marie-Pierre Malice, H. Mansouri, and Gaby Maravankin. **A general solution for the epidemic with carriers.** Statistics and Probability Letters, 4:9–15, 1986 + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Jeremy-Entner-.md b/Data/CU-DSS/CU-DSS-Jeremy-Entner-.md new file mode 100644 index 0000000..69928af --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Jeremy-Entner-.md @@ -0,0 +1,144 @@ +--- +bio-current: + name-cn: + name_en: Jeremy Entner + email: + - jfe35@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Mathematics and Statistics + title-raw: lecture + title: # Associate Professor/Assistant Professor/Professor + interests: + - Current and aspiring data scientists and analysts + - Business decision makers + - Consultants + - Executives + - Anyone seeking to gain deeper exposure to data science + homepage: + - https://www.linkedin.com/in/jeremyentner + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/jeremy-entner/6152ecdf6750f8727fa2bb2d + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Syracuse University + school: + email: + date-start: 2007 + date-end: 2013 + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: State University of New York College at Brockport + school: + date-start: 2005 + date-end: 2007 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + - State University of New York College at Brockport + - Rochester Institute of Technology + school: + major: + - Mathematics + - Photography + date-start: + - 2002 + - 1994 + date-end: + - 2005 + - 1998 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/in/jeremyentner + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Jeremy Entner ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/entner%20jeremy%20edit.jpg?itok=42Y57gMz) + +# Biography[English] +Jeremy Entner, Ph.D., joined Cornell’s Department of Statistics and Data Science as a Lecturer in 2019, where he teaches several courses including “Biological Statistics,” “The Theory of Interest,” and “Statistics for Risk Modeling.” He previously spent six years at the University of Tennessee at Martin teaching courses on mathematics and statistics. Dr. Entner holds a B.S. and M.A. in Mathematics from SUNY Brockport. He earned his Ph.D. in Mathematics with an Emphasis on Statistics from Syracuse University. + +# Biography[中文] + +# Interests[English] +- Current and aspiring data scientists and analysts +- Business decision makers +- Consultants +- Executives +- Anyone seeking to gain deeper exposure to data science + +# Interests[中文] + +# Education[English] +- PhD Mathematics and Statistics Syracuse University,2007-2013 +- Master's Degree Mathematics State University of New York College at Brockport,2005-2007 +- Bachelor's Degree Mathematics State University of New York College at Brockport,2002-2005 +- Bachelor's Degree Photography Rochester Institute of Technology,1994-1998 + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Lecture 2019-present Cornell University +- Assistant Professor 2013.8-2019.8 University of Tennessee at Martin +- Teaching Assistant 2007.8-2013.8 Syracuse University +- Customer Service Representative 2003.11-2007.8 Eastman Kodak Company + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Joe-Guinness-.md b/Data/CU-DSS/CU-DSS-Joe-Guinness-.md new file mode 100644 index 0000000..1e25d29 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Joe-Guinness-.md @@ -0,0 +1,188 @@ +--- +bio-current: + name-cn: + name_en: Joe Guinness + email: + - guinness@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: Department of Statistics and Data Science[https://stat.cornell.edu/] + major: Statistics + title-raw: Associate Professor + title: Associate Professor + interests: + - modeling and computational issues that arise in the analysis of large spatial-temporal datasets, with a focus on applications in earth sciences, including soil, weather, and climate + homepage: + - http://guinness.cals.cornell.edu/ + github: + - https://github.com/joeguinness/GpGp + googlescholar: + - https://scholar.google.com/citations?user=b0ry478AAAAJ + aminer: + - https://www.aminer.cn/profile/joe-guinness/6153cb326750f8727fa31032 + status: 在职 + last-update: 2020-08 +edu-phd: # 读博经历 + university: University of Chicago + school: + email: + date-start: 2007 + date-end: 2013 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Washington University in St. Louis + school: + major: Mathematics and Physics + date-start: 2003 + date-end: 2007 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - http://guinness.cals.cornell.edu/publications.html + research: + software: + - https://github.com/joeguinness/GpGp + - https://cran.r-project.org/package=GpGp + - https://www.youtube.com/watch?v=phyB4n0CDWg + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/pub/dir/Joe/Guinness + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Joe Guinness ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/guiness.jpg?itok=sZ3aTlVb) + +# Biography[English] +- Joe Guinness studies modeling and computational issues that arise in the analysis of large spatial-temporal datasets, with a focus on applications in earth sciences, including soil, weather, and climate. He teaches a graduate course in spatial statistics. + +# Biography[中文] + +# Interests[English] +- modeling and computational issues that arise in the analysis of large spatial-temporal datasets, with a focus on applications in earth sciences, including soil, weather, and climate + +# Interests[中文] + +# Education[English] +- 2007 - 2012 University of Chicago, Ph.D. in Statistics +- 2003 - 2007 Washington University in St. Louis, B.A. in Mathematics and Physics + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] +2021 + - Spatial and Temporal Statistics Symposium (U of Wollongong) **“Gaussian Process Computing with Vecchia’s Approximation and the GpGp R Package”** +2020 + - Brazilian Synchrotron Light Laboratory Seminar + **“Statistical Analysis of Multi-Element Micro-XRF Data”** + - Lancaster Workshop on Time Series and Spatial Statistics- - **“Nonparametric Spectral Methods for Multivariate Spatial and Spatial-Temporal Data”** + - EPFL Statistics Seminar **“Inverses of Matern Covariances on Grids”** + - Joint Statistical Meetings **“Inverses of Matern Covariances on Grids”** + - Los Alamos Statistics Seminar **“Vecchia’s Gaussian Process Approximation”** +2019 + - Cornell Atmospheric Sciences Seminar **“Spatial Temporal Statistical Methods for Earth Science Data”** + - IMS/ASA Spring Research Conference **“Nonparametric Spectral Methods for Multivariate Spatial and Spatial-Temporal Data”** + - ASA CSSA SSSA Annual Meeting **“Using Spatial Statistics to Analyze on-Farm Trials”** + - Cornell Day of Statistics **“Spectral Methods for Multivariate Spatial and Spatial-Temporal Data”** +2018 + - Penn State Dept. of Statistics Seminar **“Statistical Compression of Climate Model Output”** + - Joint Statistical Meetings **“Fully Bayesian Spectral Methods for Imaging Data”** + - Notre Dame University Statistics Seminar **“Spectral Density Estimation for Random fields via Periodic Embeddings”** + - Virginia Tech Statistics Seminar **“Spectral Density Estimation for Random fields via Periodic Embeddings”** + +# Talks[中文] + +# Work experience[English] +- 2020 - Cornell University, Department of Statistics and Data Science, Associate Professor +- 2018 - 2020 Cornell University, Department of Statistics and Data Science, Assistant Professor +- 2017 - 2018 Cornell University, Department of Biological Statistics and Computational Biology, Visiting Assistant Professor +- 2014 - 2018 NC State University, Department of Statistics, Assistant Professor +- 2012 - 2014 NC State University, Department of Statistics, Postdoctoral Scholar + +# Work experience[中文] + +# Publication[English] +- **Scaled Vecchia Approximation for Fast Computer Model Emulation** Matthias Katzfuss, **Joseph Guinness**, Earl Lawrence Under Review, Preprint +- **Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials**,Jason Cho, **Guinness**, Kharel, Maresma, Czymmek, van Aardt, Ketterings +- **Corn Grain Yield Pred. and Mapping from Unmanned Aerial System Multispectral Imagery** Sunoj Shajahan, Cho, **Guinness**, van Aardt, Czymmek, Ketterings +- **Estimating Atmos. Motion Winds from Satellite Image Data using Space-Time Drift Models** Indranil Sahoo, **Joseph Guinness**, Brian Reich +- **Nonstationary Covariance Est. using the Stochastic Score Approx. for Large Spatial Data** Amanda Muyskens, **Joseph Guinness**, Montserrat Fuentes +- **Spatial shrinkage via the product independent Gaussian process prior** Arkaprava Roy, Brian Reich, **Joseph Guinness**, Russel Shinohara, Ana-Maria Staicu +- **Estimating Agronomically Relevant Symbiotic N Fixation in Green Manure Breeding Pgms** Katherine Muller, **Joseph Guinness**, Matthew Hecking, Laurie Drinkwater +- **Inverses of Mat´ern Covariances on Grids** **Joseph Guinness** +- **Spatial Estimation Methods for Mapping Corn Silage and Grain Yield Monitor Data** Jason Cho, **J. Guinness**, Tulsi Kharel, S Sunoj, Dilip Kharel, E. Oware, J. van Aardt, Q. Ketterings +- **Gaussian Process Learning via Fisher Scoring of Vecchia’s Approximation** **Joseph Guinness** +- **Nonparametric Spectral Methods for Multivariate Spatial and Spatial-Temporal Data** **Joseph Guinness** +- **Geostatist. Modeling of Positive-Definite Matrices: Application to Diffusion Tensor Imaging** Zhou Lan, Brian Reich, **Joseph Guinness**, Dipankar Bandyopadhyay, Liangsuo Ma, F. Gerard Moeller +- **An Observational Study of the Effect of Vaporfly Shoes on Marathon Performance** **Joseph Guinness**, Debasmita Bhattacharya, Jenny Chen, Max Chen, Angela Loh +- **Vecchia Approximations for Gaussian Process Predictions** Matthias Katzfuss, **Joseph Guinness**, Wenlong Gong +- **Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering** Halley Brantley, **Joseph Guinness**, Eric Chi +Annals of Applied Statistics, Preprint +- **A General Framework for Vecchia Approximations of Gaussian Processes** Matthias Katzfuss and **Joseph Guinness** +- **Smooth Density Spatial Quantile Regression** Halley Brantley, Montserrat Fuentes, **Joseph Guinness**, Eben Thoma +- **Multi-element Effects on Arsenate Accumulation in a Geochemical Matrix Determined Using µ-XRF, µ-XANES, and Spatial Statistics** Sharma, Bell, **Guinness**, Polizzotto, Fuentes, Tappero, Chen-Weigart, Thieme, Williams, Hesterberg +- **Improved methods for Earth system modelling of atmos. soluble iron and obs. comparisons** Hamtilton, Scanza, **Guinness**, Kok, Longlei, Mingxuan, Rathod, Wan, Xiaohong, Fan, Mahowald +- **A space-time geostat. model for prob. est. of harmful algal bloom biomass and areal extent** Fang, Giudice, Scavia, Binding, Bridgeman, Chaffin, Evans, **Guinness Johengen**, Obenour +- **A Case Study Competition among Methods for Analyzing Large Spatial Data** Heaton, Datta, Finley, Furrer, Guhaniyogi, Gerber, Gramacy, **Guinness**, Hammerling, Katzfuss, Lindgren, +Nychka, Sun, Zammit-Mangion +- **Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico** V. Rohith Reddy Matli, Fang, **Guinness**, Rabalais, Craig, Obenour +- **Spectral Density Estimation for Random Fields via Periodic Embeddings** **Joseph Guinness** +- **A Test for Isotropy on the Sphere using Spherical Harmonic Functions** Indranil Sahoo, **Joseph Guinness**, Brian Reich +- **Permutation and Grouping Methods for Sharpening Gaussian Process Approximations** **Joseph Guinness** +- **Fully Bayesian Spectral Methods for Imaging Data** Brian Reich, **Joseph Guinness**, Simon Vandekar, Russel T Shinohara, Ana-Maria Staicu +- **Compression and Conditional Emulation of Climate Model Output** **Joseph Guinness** and Dorit Hammerling +- **Optimal Seed Deployment under Climate Change using Spatial Models: Application to Loblolly Pine in the Southeastern US** Alfredo Farjat, Brian Reich, **Joseph Guinness**, Ross Whetten, Steve McKeand, Fikret Isik +- **An Evolutionary Spectrum Approach for Modeling Land/Ocean Nonstationarities** Stefano Castruccio and **Joseph Guinness** +- **Isotropic covariance functions on spheres: some properties and modeling considerations** **Joseph Guinness** and Montserrat Fuentes +- **Circulant embedding of approximate covariances for inference from Gaussian data on large lattices** **Joseph Guinness** and Montserrat Fuentes +- **Likelihood approximations for big nonstationary spatial-temporal lattice data** **Joseph Guinness** and Montserrat Fuentes +- **Multivariate spatial modeling of cond. dep. in microscale soil elemental composition data** **Joseph Guinness**, Montserrat Fuentes, Dean Hesterberg, and Matthew Polizzotto +- **Interpolation of nonstationary high frequency spatial-temporal temperature data** **Joseph Guinness** and Michael Stein +- **Transformation to approximate independence for locally stationary Gaussian processes** **Joseph Guinness** and Michael Stein + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-John-Abowd-.md b/Data/CU-DSS/CU-DSS-John-Abowd-.md new file mode 100644 index 0000000..f24739f --- /dev/null +++ b/Data/CU-DSS/CU-DSS-John-Abowd-.md @@ -0,0 +1,120 @@ +--- +bio-current: + name-cn: + name_en: John Abowd + email: + - John.abowd@cornell.edu + - john.maron.abowd@census.gov + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - ILR School[https://www.ilr.cornell.edu/] + major: Research and Methodology + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://blogs.cornell.edu/abowd/ + github: + googlescholar: + aminer: https://www.aminer.cn/profile/john-m-abowd/54090723dabfae450f459f57 + status: 在职 + last-update: 2021-07-21 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: https://blogs.cornell.edu/abowd/ + arxiv: + linkedin: + weibo: + twitter: @john_abowd + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![John Abowd ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/JohnAbowd_crop.jpg?itok=U20Ek82Z) + +# Biography[English] +John Abowd is the Edmund Ezra Day Professor in the department of Economics, where he also serves as the Director of Graduate Studies. Professor Abowd is a member in the graduate fields of Economics, Information Science, Industrial and Labor Relations and Statistics. He is a professor in the department of Information Science as well. He is the Director of the Labor Dynamics Institute at Cornell, an institution whose purpose is to to create and make accessible novel data on the dynamics of the labor markets. + +John M. Abowd is Chief Scientist and Associate Director for Research and Methodology at the United States Census Bureau and a member of the federal career senior executive service. He is on leave from Cornell University where he is the Edmund Ezra Day Professor of Economics, Professor of Data Science and Statistics and Professor of Information Science. At the Census Bureau he leads a directorate of five research centers each devoted to domains of investigation important to the future of social and economic statistics. At Cornell, his primary appointment is in the Department of Economics in the ILR School. He is also Research Associate at the National Bureau of Economic Research (NBER, Cambridge, MA, on leave during the appointment at the Census Bureau), Research Affiliate at the Centre de Recherche en Economie et Statistique (CREST, Paris, France), Research Fellow at the Institute for Labor Economics (IZA, Bonn, Germany), and Research Fellow at IAB (Institut für Arbeitsmarkt-und Berufsforschung, Nürnberg, Germany). Prof. Abowd was the founding Director of the Labor Dynamics Institute (LDI) at Cornell. Abowd is a Fellow of the American Association for the Advancement of Science. He is past President (2014-2015) and Fellow of the Society of Labor Economists. He is past Chair (2013) of the Business and Economic Statistics Section and Fellow of the American Statistical Association. He is an Elected Member of the International Statistical Institute and Fellow of the Econometric Society. He served as a member of the Inaugural Board of the Canadian Research Data Centre Network (2017-2019). He served as Distinguished Senior Research Fellow at the United States Census Bureau from 1998 until 2016. He served on the National Academy of Sciences Committee on National Statistics (2010-2016). He servedon the American Economic Association’s Committee on Economic Statistics (2013-2018). He served as Director of the Cornell Institute for Social and Economic Research (CISER) from 1999 to 2007. Prof. Abowd has taught and done research at Cornell University since 1987, including seven years on the faculty of the Johnson Graduate School of Management. His current research focuses on the creation, dissemination, privacy protection, and use of linked, longitudinal data on employees and employers. In his earlier work at the Census Bureau he provided scientific leadership for the Longitudinal EmployerHousehold Dynamics Program, which produces research and public-use data integrating censuses, demographic surveys, economic surveys, and administrative data. The LEHD Program’s public use data products include the Quarterly Workforce Indicators (http://lehd.ces.census.gov/), the most detailed time series data produced on the demographic characteristics of local American labor markets and OnTheMap (http://onthemap.ces.census.gov/), a user-driven mapping tool for studying work-related commuting patterns. His original and ongoing research on integrated labor market data is often done in collaboration with the Institut National de la Statistique et des Etudes Economiques (INSEE), the French national statistical institute. Prof. Abowd’s other research interests include network models for integrated labor market data; statistical methods for confidentiality protection of micro-data; international comparisons of labor market outcomes; executive compensation with a focus on international comparisons; bargaining and other wage-setting institutions; and the econometric tools of labor market analysis. Prof. Abowd has been Principal Investigator or Co-Principal Investigator for multiyear grants and contracts from the National Science Foundation, the National Institutes of Health, the Alfred P. Sloan Foundation, the Russell Sage Foundation, and the U.S. Census Bureau. He has published articles in the American Economic Review, Econometrica, the Review of Economics and Statistics, the Quarterly Journal of Economics, the Journal of the American Statistical Association, the Journal of Business and Economic Statistics, the Journal of Econometrics, the Journal of Survey Statistics and Methodology, and other major economics and statistics journals. Prof. Abowd served on the faculty at Princeton University, the University of Chicago, and the Massachusetts Institute of Technology before moving to Cornell. When he is not traveling for his research program, he enjoys polishing his French, touring wine country and riding his bike. [johnabowd.com, February 2021 + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Kara-Karpman-.md b/Data/CU-DSS/CU-DSS-Kara-Karpman-.md new file mode 100644 index 0000000..aacb314 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Kara-Karpman-.md @@ -0,0 +1,120 @@ +--- +bio-current: + name-cn: + name_en: Kara Karpman + email: + - kjk233@cornell.edu + - kkarpman@middlebury.edu + sex: female + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Adjunct Assitant Professor + title: Assistant Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://www.middlebury.edu/academics/math/faculty/node/648984 + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/kara-karpman/6153ce679e795e7f16cf4652 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + - Middlebury[http://www.middlebury.edu/#story645114] + school: + - Department of Mathematics[http://www.middlebury.edu/academics/math] + major: Statistics + email: kkarpman@middlebury.edu + homepage: + - http://www.middlebury.edu/academics/math/faculty/node/648984 + date-start: + title: Assistant Professor + type: Assistant Professor +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Kara Karpman ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Karpman%20Kara.jpg?itok=cySsJEz-) + +# Biography[English] + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Kengo-Kato-.md b/Data/CU-DSS/CU-DSS-Kengo-Kato-.md new file mode 100644 index 0000000..3adadcf --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Kengo-Kato-.md @@ -0,0 +1,141 @@ +--- +bio-current: + name-cn: + name_en: Kengo Kato + email: + - kk976@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + - Statistical Theories for Analyzing Big Data + - High-dimensional Data + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://sites.google.com/site/kkatostat/home + github: + googlescholar: + - https://sites.google.com/site/kkatostat/home #这个老师很多信息在Google上，我还不会翻墙找。。 + aminer: # 无 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Tokyo + school: Economics + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: University of Tokyo + school: + major: economics + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Kengo Kato ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/kato%20kengo.jpg?itok=JDh9_Hq9) + +# Biography[English] +Most recently a member of the Faculty of Economics at The University of Tokyo, Kato’s research fields are mathematical statistics, econometrics, and economic statistics with a focus on high dimensional statistical and econometric models. He was a visiting scholar in the Department of Economics at MIT and is the associate editor of the Japanese Economic Review and the Journal of Statistical Planning and Inference. + +Kato’s research is in the fields of mathematical statistics, econometrics, and economic statistics with a focus on high dimensional statistical and econometric models. + +Before coming to Cornell, Kato was an associate professor in the Graduate School of Economics at the University of Tokyo from 2014 to 2018. He was an assistant professor in the Department of Mathematics at Hiroshima University from 2009 to 2013. During his time at Hiroshima, Kato was a visiting scholar in the Department of Economics at the Massachusetts Institute of Technology (MIT) in 2011-2012. + +Kato was born in Nagoya, Japan and spent seven years of his childhood in Brussels, Belgium where is father was an executive with the Toyota Corporation. His family returned to Tokyo when Kato was thirteen years old. + +When he started his undergraduate studies at the University of Tokyo he chose the law track. “I thought I wanted to be a lawyer,” says Kato. “But during my second year I switched to economics. My real love was mathematics, but at the same time I wanted to be able to apply what I was working on.” + +Kato planned to go directly into industry as soon as he earned a Master’s—he even had an offer from a major bank. “During my Master’s program I solved a small problem for my thesis,” says Kato. “The moment of discovery is quite exciting. That experience made me think it might be a good idea to go on and get my Ph.D.” He completed a graduate program in statistics in the School of Economics at the University of Tokyo. + +After earning his Ph.D., Kato focused on high dimensional statistics. His research at Cornell aims to extend the central limit theorem (CLT) to modern high-dimensional data sets. An example would be DNA. The human genome has thousands of genes. If you wanted to know which combination of genes might contribute to a specific disease there are a staggering number of combinations to account for. “High-dimensional CLT will be more and more useful, “says Kato, “in analyzing these very large, very complex data.” + +Because he has a deep background in economics, Kato is also doing research into economics-based problems. The intersection between economics and statistics is called econometrics. A major concern in econometrics is how to make inferences when there are errors in the data. This is called the measurement error problem (MEP) and Kato is working to address this concern. An example would be how to measure student ability. “There is no way to measure this thing we call “ability” so we use proxy values,” says Kato. “These proxy values have errors built in and we need to correct for those errors.” + +Kato is happy to be at Cornell. “There are no Statistics Departments in Japan, so I have always felt a bit isolated professionally,” says Kato. “Cornell has a great history in Statistics. In fact, many of my heroes in stats are somehow affiliated with Cornell.” Kato is looking for graduate students to join in his work. “I am looking for a good match in topics so that a student can learn more about what they want to learn about and at the same time their work can support mine.” + +When he is not working on tricky statistical problems, Kato says that his two children, (aged four and six) take his mind away from work fairly quickly. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Professor Cornell University,2018-present +- Associate Professor the University of Tokyo,2014-2018 +- Assistant Professor the Department of Mathematics at Hiroshima University,2009-2013 +- Visiting Scholar in the Depatment of Economics at the Massachusettsat Institute of Technology,2011-2012 + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Kevin-Packard-.md b/Data/CU-DSS/CU-DSS-Kevin-Packard-.md new file mode 100644 index 0000000..19fed98 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Kevin-Packard-.md @@ -0,0 +1,144 @@ +--- +bio-current: + name-cn: + name_en: Kevin Packard + email: + - kcp48@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - ILR school[https://www.ilr.cornell.edu/] + major: + title-raw: Visiting Lecturer + title: # Associate Professor/Assistant Professor/Professor + interests: + - Securities and Exchange Commission Reporting + - Religion + homepage: + - https://www.byui.edu/accounting/our-faculty/kevin-l-packard + - https://www.signalhire.com/profiles/kevin-packard%27s-email/16110912 + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Virginia Tech + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + - Virginia Polytechnic Institute and State University + - State University of New York College + school: + - #null + - Environmental Sciences and Forestry + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Carleton College + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/in/kevinpackard + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile +![Kevin Packard ](https://media.licdn.cn/dms/image/C5603AQFYsn84B9th6g/profile-displayphoto-shrink_200_200/0/1516798219897?e=1639612800&v=beta&t=-3uV95DnD16hAz9zDN8_6ZqzYRO8wtS0mWqXYHv7N5g) + +# Biography[English] +I'm a lecturer of statistics with 6 years experience teaching introductory statistics and regression analysis courses to undergraduate students. I have served as a statistical consultant at Cornell University and as an academic advisor for the statistics major at the University of Wisconsin, Madison. I love working with undergraduate students who have an interest in statistics and those that are considering careers in statistics related fields. + +I'm a creative and detailed-oriented person with a love of the outdoors. I enjoy applications of statistics to ecology and forestry and have had 4 years of experience collecting forest inventory data across the northeastern United States with the USDA Forest Service's Forest Inventory and Analysis (FIA) program. I'm just as comfortable hiking a mountain to collect data as being in the office developing mathematical models to analyze data. I look forward to continue building a career as a statistician with emphasis on public service. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] +- Doctor of Philosophy (Ph.D.) at Virginia Tech +- Master’s Degree at Virginia Polytechnic Institute and State University +- Master’s Degree at State University of New York College of Environmental Sciences and Forestry +- Bachelor’s Degree at Carleton College + +# Education[中文] + +# Awards[English] +- Honored Instructor, University of Wiscinsin-Madison, University Housing Academic Initiatives,2014.12 + +奖项说明Nominated as honored instructor by undergraduate students living in university housing for making a meaningful impact on their education in STAT 301. +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Visiting Lecturer,Cornell University ILR School,2017.1-present +- Statistical Consultant,Cornell University,2015-2017.8 +- Faculty Associate, Department of Statistics,University of Wisconsin - Madison,2010-2015 +- U.S. Forest Service + - Forester,2001-2004,Cortland, NY + - Forester,2000-2001,Gassaway, WV +- Forest Technician,U.S. Forest Service,1998-1998 +- Forestry Assistant,Duke University - Duke Forest,1996-1997 + +# Work experience[中文] + +# Publication[English] +- **Genomic Prediction in a Large African Maize Population Crop Science**, Vahid Edriss, Yanxin Gao, Xuecai Zhang, MacDonald Bright Jumbo, Dan Makumbi, Michael Scott Olsen, José Crossa, **Kevin C. Packard** and Jean-Luc Jannink*(2017.8) +- **Factors affecting the physical and mental health of older adults in China: The importance of marital status, child proximity, and gender**, Lindy Williams*, Renling Zhang, **Kevin C. Packard**(2017) +- **Forest sampling combining fixed- and variable-radius sample plots**, **Kevin C. Packard**, Philip J. Radtke(2007.8) +- **A comparison of estimation methods for fitting Weibull and Johnson’s SB distributions to mixed spruce-fir stands in northeastern North America**, Lianjun Zhang*, **Kevin C. Packard**, and Chuangmin Liu(2003.7) + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Marten-Wegkamp-.md b/Data/CU-DSS/CU-DSS-Marten-Wegkamp-.md new file mode 100644 index 0000000..439df3a --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Marten-Wegkamp-.md @@ -0,0 +1,174 @@ +--- +bio-current: + name-cn: + name_en: Marten Wegkamp + email: + - marten.wegkamp@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - Mathematics[https://math.cornell.edu/] + major: + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://www.math.cornell.edu/~marten # 如果有多个主页，请都填写上 + github: + googlescholar: + - https://scholar.google.com/citations?user=Rxm_97EAAAAJ + aminer: + - https://www.aminer.cn/profile/wegkamp-marten-h/561d28c945ce1e59647699fb + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Leiden University + school: Mathematics + email: + date-start: + date-end: 1996 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/search/stat?searchtype=author&query=Wegkamp%2C+M + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: + - Statistical Learning and High Dimensional Inference Group at Cornell +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Marten Wegkamp ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/marten-wegkamp_crop.jpg?itok=eCzIOJVj) + +# Biography[English] +Marten Wegkamp (PhD Mathematics, Leiden University, 1996) joined the Cornell faculty in 2011. His research area are mathematical statistics, empirical process theory, high dimensional statistics and statistical learning theory. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- Xin Bing, Tyler Lovelace, Florentina Bunea, **Marten Wegkamp**, Harinder Singh, Panayiotis Benos, Jishnu Das. **Essential Regression - a generalizable framework for inferring causal latent factors from multi-omic human datasets**. bioRxiv 2021.05.03.442513 +- Xin Bing, Florentina Bunea, Seth Strimas-Mackey and **Marten Wegkamp**. **Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations.** arXiV:2107.05766 +- Xin Bing, Florentina Bunea and **Marten Wegkamp**. **Detecting approximate replicate components of a high-dimensional random vector with latent structure.** ArXiv: 2010.02288 +- Xin Bing, Florentina Bunea, Seth Strimas-Mackey and **Marten Wegkamp**. **Prediction in latent factor regression: Adaptive PCR and beyond**. Journal of Machine Learning Research (2021). ArXiv: 2007.10050 +- Xin Bing, Florentina Bunea and **Marten Wegkamp**. **Inference in Interpretable Latent Factor Regression Models.** Bernoulli (2021). arXiv: 1905.12696 +- Florentina Bunea, Seth Strimas-Mackey and **Marten Wegkamp**. **Interpolation under latent factor regression models: Generalization via low-dimensional adaptation.** arXiv:2002.02525 +Xin Bing, Florentina Bunea, Marten Wegkamp. Optimal estimation of sparse topic models. Journal of Machine Learning Research 21, 1-45 (2020). +- Xin Bing, Florentina Bunea and **Marten Wegkamp**. **A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics.** Bernoulli 26(3), 1765-1796 (2020). [Main text and Supplement (41 pp)] +- Xin Bing, Florentina Bunea, Yang Ning and **Marten Wegkamp**. **Sparse Latent Factor Models with Pure Variables for Overlapping Clustering.** Annals of Statistics 48(4), 2055-2081 (2020). [Main text and Supplement (31 pp)] +- Xin Bing and **Marten Wegkamp**. **Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models.** Annals of Statistics, 47(6), 3157-3184 (2019). [Main text and Supplement (28 pp)] +- Dragan Radulovic and **Marten Wegkamp**. **Weak convergence of stationary empirical processes.** Journal of Statistical Planning and Inference, 149, 75-84 (2018). +- Xin Bing and **Marten Wegkamp**. **Discussion of Random-projection Ensemble Classification by Timothy I. Cannings and Richard J. Samworth.** J. R. Statist. Soc. B 79(4), 1006-1007 (2017). +- Yue Zhao and **Marten Wegkamp**. **Semiparametric Gaussian copula classification.** arXiv: 1411.2944 +- Dragan Radulovic, **Marten Wegkamp** and Yue Zhao. **Weak convergence of empirical copula processes indexed by functions.** Bernoulli, Vol.23, No. 4B, 3346-3384 (2017) +- **Marten Wegkamp** and Yue Zhao. **Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas.** Bernoulli, Vol. 22, No. 2, 1184-1226 (2016) +- Jean-David Fermanian, Dragan Radulovic and **Marten Wegkamp**. **An asymptotic total variation test for copulas.** Bernoulli Volume 21, Number 3, 1911-1945 (2015) +- Jishnu Das, Kaitlyn M. Gayvert, Florentina Bunea, **Marten Wegkamp** and Haiyuan Yu. **ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers.** BMC Genomics (2015) +- Jacob Bien and **Marten Wegkamp**. **Discussion of "Correlated variables in regression: clustering and sparse estimation'' by Buhlmann, Rutimann, van de Geer, and Zhang.** Journal of Statistical Planning and Inference, 143, 1859 -1862 (2013) +- Florentina Bunea, Yiyuan She and **Marten Wegkamp**. **Joint variable and rank selection for parsimonious estimation of high dimensional matrices.** Annals of Statistics, 40(5), 2359 - 2388 (2012) +- Jean-David Fermanian and **Marten Wegkamp**. **Time dependent copulas**. Journal of Multivariate Analysis, 110, 19 - 29 (2012) +- Sara van de Geer and **Marten Wegkamp**, Editors. **Selected Works of Willem van Zwet. Springer Series in Selected Works in Probability and Statistics.** Springer (2012) +- Florentina Bunea, Yiyuan She, **Marten Wegkamp**. **Optimal Selection of Reduced Rank Estimators of High-Dimensional Matrices.** Annals of Statistics, 39(2), 1282 - 1309 (2011) +- Florentina Bunea, Andrada Ivanescu and **Marten Wegkamp**. **Adaptive inference for the mean of a Gaussian process in functional data.** Journal of the Statistical Royal Society, Series B, 73(4), 531 - 538 (2011) +- **Marten Wegkamp** and Ming Yuan. **Support Vector Machines With a Reject Option.** Bernoulli, 17(4), 1368 - 1385 (2011) +Florentina Bunea, Alexandre Tsybakov, Marten Wegkamp and Adrian Barbu. Spades and Mixture Models. Annals of Statistics, 38(4), 2525 - 2558 (2010) +- Ming Yuan and **Marten Wegkamp**. **Classification Methods with Reject Option Based on Convex Risk Minimization.** Journal of Machine Learning Research, 11, 111 - 130 (2010) +- Dragan Radulovic and **Marten Wegkamp**. **Uniform Central Limit Theorems for pregaussian classes for functions.** IMS Collections: High Dimensional Probability V: The Luminy Volume, 5, 84 - 102 (2009) +- Peter Bartlett and **Marten Wegkamp**. **Classification with a reject option using a hinge loss**. Journal of Machine Learning Research, 9, 1823 -1840 (2008) +- Laszlo Gyorfi and **Marten Wegkamp**. **Quantization for nonparametric regression**. I.E.E.E. Transactions on Information Theory, 54(2), 867 - 873 (2008) +- Florentina Bunea, Alexandre Tsybakov and **Marten Wegkamp**. **Aggregation for Gaussian regression.** Annals of Statistics, 35(4), 1674 -1697 (2007) +- Florentina Bunea, Alexandre Tsybakov and **Marten Wegkamp**. **Sparsity oracle inequalities for the Lasso.** Electronic Journal of Statistics, 1, 169 - 194 (2007) +- **Marten Wegkamp**. **Lasso type classifiers with a reject option**. Electronic Journal of Statistics, 1, 155 - 168 (2007) +- Florentina Bunea, Alexandre Tsybakov and **Marten Wegkamp**. **Sparse density estimation with $\ell_1$ penalties.** COLT 2007 Proceedings of the 20th annual conference on Learning theory, 530 - 543. Springer (2007) +- Radu Herbei and **Marten Wegkamp**. **Classification with reject option.** Canadian Journal of Statistics, 34(4), 709 - 721 (2006) +- Florentina Bunea, Alexandre Tsybakov and **Marten Wegkamp**. ***Aggregation and Sparsity via $\ell_1$ Penalized Least Squares.*** COLT 2006 Proceedings of the 19th annual conference on Learning Theory, 379 - 391. Springer (2006) +- Florentina Bunea, **Marten Wegkamp**, and Anna Auguste. **Consistent Variable Selection in High Dimensional Regression via Multiple Testing.** Journal of Statistical Planning and Inference, 136(12), 4349 - 4364 (2006) +- Gerard Biau, Florentina Bunea, and **Marten Wegkamp**. **Function Classification in Hilbert Spaces**. I.E.E.E. Transactions on Information Theory, 51(6), 2163 - 2172 (2005) +- Gerard Biau and **Marten Wegkamp**. **Minimum Distance Estimation of Copula Densities**. Statistics and Probability Letters, 73, 105 - 114 (2005) +- Jean-David Fermanian, Dragan Radulovic and **Marten Wegkamp**. Weak Convergence of Empirical Copula Processes. Bernoulli, 10(5), 847 - 860 (2004) +- Gabor Lugosi and **Marten Wegkamp**. **Complexity Regularization via Localized Random Penalties**. Annals of Statistics, 32(4), 1679 - 1697 (2004) +- Florentina Bunea and **Marten Wegkamp**. **Two-Stage Model Selection Procedures in Partially Linear Regression.** Canadian Journal of Statistics, 32(2), 105 - 118 (2004) +- Nicolas Hengartner and **Marten Wegkamp**. **Second Order Asymptotics for M-Estimators under Non-standard Conditions.** Proceedings of the first Erich L. Lehmann Symposium (IMS Lecture Notes Series), 107 - 124 (2004) +- Dragan Radulovic and **Marten Wegkamp**. **Necessary and Sufficient Conditions for Weak Convergence of Smoothed Empirical Processes.** Statistics and Probability Letters, 61(3), 321 - 336 (2003) +- Florentina Bunea and **Marten Wegkamp**. **A note on penalized minimum distance estimation in nonparametric regression.** Canadian Journal of Statistics, 31(3), 267 - 274 (2003) +- **Marten Wegkamp**. **Model Selection in Nonparametric Regression**. Annals of Statistics, 31(1), 252 - 273 (2003) +- Nicolas Hengartner, Eric Matzner-Lober and **Marten Wegkamp**. **Bandwidth Selection for Local Linear Regression.** Journal of the Royal Statistical Society, Series B, 64 (4), 1 - 14 (2002) +- Donald Brown and **Marten Wegkamp**. **Weighted Mean-Square Distance from Independence Estimation**. Econometrica, 70(5), 2035 - 2051 (2002) +- Nicolas Hengartner and **Marten Wegkamp**. **Estimation and Selection Procedures in Regression: an L1 approach.** Canadian Journal of Statistics, 29 (4), 621 - 632 (2001) +- Dragan Radulovic and **Marten Wegkamp**. **Weak Convergence of Smoothed Empirical Processes: beyond Donsker Classes.** In High Dimensional Probability II, E. Gine, D. Mason and J. Wellner Editors, Birkhauser, 89 - 106 (2000) +- **Marten Wegkamp**. **Quasi-Universal Bandwidth Selection for Kernel Density Estimators**. Canadian Journal of Statistics, 27(2), 409 - 420 (1999) +- **Marten Wegkamp**. **Entropy Methods in Statistical Estimation**. CWI-tract volume 125, Amsterdam, The Netherlands (1999) +- Roelof Helmers and **Marten Wegkamp**. **Wild Bootstrapping in Finite Populations with Auxiliary Information.** Scandinavian Journal of Statistics, 25, 383 - 399 (1998) +- Sara van de Geer and **Marten Wegkamp**. **Consistency for the Least Squares Estimator in Nonparametric Regression.** Annals of Statistics, 24, 2513 - 2523 (1996) + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Martin-Wells-.md b/Data/CU-DSS/CU-DSS-Martin-Wells-.md new file mode 100644 index 0000000..3fc0d1a --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Martin-Wells-.md @@ -0,0 +1,165 @@ +--- +bio-current: + name-cn: + name_en: Martin Wells + email: + - mtw1@cornell.edu + - martin.t.wells@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - Department of Computational Biology[https://compbio.cornell.edu/] + major: + title-raw: Professor + title: Professor + interests: + - applied and theoretical statistics + - applied probability + homepage: + - https://www.ilr.cornell.edu/people/martin-wells=-=--= + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.ilr.cornell.edu/people/martin-wells + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Martin Wells ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/marty-wells_crop.jpg?itok=rjWP9XlK) + +# Biography[English] +Martin T. Wells, Ph.D., joined the Cornell faculty in 1987 and is the Department Chair and Charles A. Alexander Professor of Statistical Sciences. He is also a Professor of Social Statistics, Professor of Clinical Epidemiology and Health Services Research at Weill Medical School, an Elected Member of the Cornell Law School Faculty, as well as the Director of Research in the School of Industrial and Labor Relations. He teaches statistical methodology to undergraduate and graduate students in fields such as agriculture, biology, epidemiology, finance, law, medicine, nutrition, social science, and veterinary medicine as well as graduate courses in statistics. + +Martin Wells' research interests center on applied and theoretical statistics and sometimes cross the boundary into applied probability. He has worked on inference questions in credit risk, economic damages, epidemiology, finance (physical and risk neutral worlds), graphical models, legal studies, microarrays, proteomics, quantitative trait loci, extremes, data networks and has considered estimation problems for heavy-tailed phenomena. His theoretical research has focused on Bayesian statistics, biostatistics, conditional inference, evidence assessment, functional data, hypothesis testing, saddlepoint approximations, and shrinkage estimation. + +# Biography[中文] + +# Interests[English] +- applied and theoretical statistics +- applied probability +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- Haim Y Bar, James Booth, **Martin Wells**. 2014. **A Bivariate Model for Simultaneous Testing in Bioinformatics Data**, Journal of the American Statistical Association . 109(506):537-547. (DOI:10.1080/01621459.2014.884502) +- Dominique Fourdrinier, **Martin Wells**. 2012. **Improved Loss Estimation for Shrinkage Estimators, Statistical Science .** 27:61-81. +- Xin Zhou, **Martin Wells**. 2012. **Reference Priors for Linear Models with General Covariance Structures**, Journal of Statistical Planning and Inference . 142:2473-2484. +- S M Wernimont, Andrew Clark, Patrick J Stover, **Martin Wells**, A A Litonjua, S T Weiss, J M Gaziano, P. S. Vokonas, K L Tucker, Patricia Ann Cassano. 2012. **Folate network genetic variation predicts cardiovascular disease risk in non-hispanic white males, Journal of Nutrition .** 142(7):1272-1279. (DOI:10.3945/jn.111.157180) (PubMed Central:PMC3374665) +- L Yu, H Li, **Martin Wells**. 2011. **Return Dynamics with Lévy Jumps: Evidence from Stock and Option Prices**, Mathematical Finance . +- B S Warren, Joseph J. Wakshlag, M Maley, Tracy Farrell, A M Struble, M R Panasevich, **Martin Wells**. 2011. **Use of pedometers to measure the relationship of dog walking to body condition score in obese and non-obese dogs**, British Journal of Nutrition . 106:S85-S89. (DOI:10.1017/S0007114511001814) +- S M Wernimont, A G Clark, Patrick J Stover, **Martin Wells**, A A Litonjua, S T Weiss, J M Gaziano, K L Tucker, J Schwartz, V Bolati, Patricia Ann Cassano. 2011. **Folate Network Genetic Variation, Plasma Homocysteine, and Global Genomic Methylation Content**, BMC Medical Genetics . 12(1):150. (DOI:10.1186/1471-2350-12-150) +- **Martin Wells**. 2010. **A Bayesian Hierarchical Regression Approach to Clustered and Longitudinal Data**, Journal of Empirical Legal Studies . 7:634-663. +- **Martin Wells**, T Eisenberg. 2010. **Failure to differentiate between men and women in primary prevention**, Annals of internal medicine . http://www.annals.org/content/152/2/69/reply#annintmed_el_123978. +- Haim Bar, James Booth, Elizabeth Schifano, **Martin Wells**. 2010. **Laplace approximated EM microarray analysis: an empirical Bayes approach for comparative microarray experiments**, Statistical Science . 25(3):388-407. (DOI:10.1214/10-STS339) +- **Martin Wells**, T Eisenberg. 2010. **Letter regarding article, "Statins for the primary prevention of cardiovascular events in women with elevated high-sensitivity C-reactive protein or dyslipidemia: results from the Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) and meta-analysis of women from primary prevention trials"**, Circulation . 122:23. +- E D Schifano, Robert Lee Strawderman, **Martin Wells**. 2010. **MM algorithms for minimizing nonsmoothly penalized objective functions**, Electronic Journal of Statistics . 4:1258-1299. (DOI:10.1214/10-EJS582) +- B Warren, M Maley, L Sugarwala, **Martin Wells**, Carol Devine. 2010. **Small Steps Are Easier Together: An Ecological Goal-Based Intervention To Increase Walking By Women In Rural Worksites**, Preventive Medicine . 5:230-234. +- **Martin Wells**, T Eisenberg. 2010. **Statins and risk of incident diabetes, Lancet** . 375:2140-2141. +- T Eisenberg, M Heise, N Waters, **Martin Wells**. 2010. **The Decision to Award Punitive Damages: An Empirical Study,** Journal of Legal Analysis . 2:577-619. +- James Booth, Walter T Federer, **Martin Wells**, Russell Wolfinger. 2009. **A multivariate variance components model for analysis of covariance in designed experiments**, Statistical Science . 24(2):223-237. (DOI:10.1214/09-STS294) +- **Martin Wells**. 2009. **The Exact Distribution of the Condition Number of a Gaussian Matrix**, SIAM Journal on Matrix Analysis and Applications . 31:1125-1130. +- R. Y. Ivanek, T. Grohn, **Martin Wells**, S. Raengpradub, M. J. Kazmierczak, Martin Wiedmann. 2008. **Extreme value theory in analysis of differential expression in microarrays where either only up- or down-regulated genes are relevant or expected**, Genetics Research . 90:347-361. +- M Zhang, Robert Lee Strawderman, M Cowen, **Martin Wells**. 2006. **Profiling pharmacy expenditures in managed health care: Bayesian inference for a two-part hierarchical model**, Journal of the American Statistical Association . 101:934-945. +- Stephan A Jesch, Peng Liu, Xin Zhao, **Martin Wells**, Susan A. Henry. 2006. **Multiple endoplasmic reticulum-to-nucleus signaling pathways coordinate phospholipid metabolism with gene expression by distinct mechanisms**, Journal of Biological Chemistry . 281(33):24070-24083. +- S A Jesch, X Zhao, **Martin Wells**, Susan A. Henry. 2005. **Genome Wide Analysis Reveals Inositol, Not Choline, as the Major Effector of Ino2p-Ino4p and Unfolded Protein Response Target Gene Expression in Yeast**, Journal of Biological Chemistry . 280(10):9106-9118. (PubMed Central:PMC1352320) +- R Ivanek, Y T Gröhn, Martin Wiedmann, **Martin Wells**. 2004. Mathematical model of Listeria monocytogenes cross-contamination in a fish processing plant, Journal of Food Protection . 67:2688-2697. +- **Martin Wells**, Weijing Wang. 2000. **Model Selection and Semiparametric Inference for Bivariate Failure-Time Data, Journal of the American Statistical Association** . 95(449):62-72. +- **Martin Wells**, Theodore Eisenberg. 2000. **Inbreeding in Law School Hiring: Assessing the Performance of Faculty Hired from within, Journal of Legal Studies**. 29(1):369-388. +- **Martin Wells**, Dominique Cellier, Dominique Fourdrinier. 1999. **Bayesian Estimation for Spherically Symmetric Distributions**, Journal of Multivariate Analysis . 70(1):95-117. +- **Martin Wells**, Morris L Eaton, David A Freedman, Stephen P Klein, Richard A Olshen, Kenneth W Wachter, Donald Ylvisaker. 1999. **Statistical Controversies in Census 2000**, Jurimetrics . 39(4):347-375. +- **Martin Wells**, Theodore Eisenberg. 1999. **The Predictability of Punitive Damages Awards in Published Opinions, the Impact of BMW v. Gore on Punitive Damages Awards, and Forecasting Which Punitive Awards Will be Reduced**, Supreme Court Economic Review . 7:59-86. +- Robert Lee Strawderman, **Martin Wells**. 1998. **Approximately exact inference for the common odds ratio in several 2 x 2 tables (with discussion and rejoinder)**, Journal of the American Statistical Association . 93:1294-1306. +- **Martin Wells**, Theodore Eisenberg, Stephen P Garvey. 1998. **But Was He Sorry? The Role of Remorse in Capital Sentencing**, Cornell Law Review . 83(6):1599-1637. +- **Martin Wells**, Theodore Eisenberg, Stefan Sundgren. 1998. **Larger Board Size and Decreasing Firm Value in Small Firms, Journal of Financial Economics** . 48(1):35-54. +- **Martin Wells**, T Eisenberg. 1998. **Punitive Awards After BMW, a New Capping System, and the Reported Opinion Bias**, Wisconsin Law Review . 1:387-426. +- **Martin Wells**, Weijing Wang. 1998. **Nonparametric Estimation Of Successive Duration Times Under Dependent Censoring**, Biometrika . 85(3):561-572. +- **Martin Wells**, Theodore Eisenberg. 1998. **Ranking and Explaining the Scholarly Impact of Law Schools**, Journal of Legal Studies . 27(2):373-413. +- **Martin Wells**, Dominique Fourdrinier, William E Strawderman. 1998. **On the Construction of Bayes Minimax Estimators, Annals of Statistics** . 26(2):660-671. +- **Martin Wells**, Weijing Wang. 1997. **Nonparametric Estimator of the Bivariate Survival Function Under Simplified Censoring Conditions**, Biometrika . 84(4):863-880. +- Robert Lee Strawderman, **Martin Wells**. 1997. **Accurate bootstrap confidence limits for the cumulative hazard and survivor functions, Journal of the American Statistical Association** . 92:1356-1375. +- Robert Lee Strawderman, M Parzen, **Martin Wells**. 1997. **Accurate confidence limits for quantiles under random censoring**, Biometrics . 53:1399-1415. +- **Martin Wells**, Theodore Eisenberg, John Goerdt, Brian Ostrom, David Rottman. 1997. **The Predictability of Punitive Damages, Journal of Legal Studies** . 26(2):623-661. +- **Martin Wells**, T Eisenberg, S Garvey. 1996. **Jury Responsibility In Capital Sentencing: An Empirical Study**, Buffalo Law Review . 44:339-380. +- Robert Lee Strawderman, G Casella, **Martin Wells**. 1996. **Practical small sample asymptotics for regression problems, Journal of the American Statistical Association** . 91:643-655. +- **Martin Wells**, A Erez, M Bloom. 1996. **Using Random Rather than Fixed Effects Models in Meta-Analysis: Implications for Situational Specificity and Validity Generalization**, Personnel Psychology . 49:275-306. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Melissa-Smith-.md b/Data/CU-DSS/CU-DSS-Melissa-Smith-.md new file mode 100644 index 0000000..bb88e79 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Melissa-Smith-.md @@ -0,0 +1,117 @@ +--- +bio-current: + name-cn: + name_en: Melissa Smith + email: + - ms429@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Senior Lecturer + title: # Associate Professor/Assistant Professor/Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://stat.cornell.edu/people/faculty/melissa-smith + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/melissa-smith/53f4372cdabfaee02acd0d8a + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of North Carolina + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Melissa Smith ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/MelissaSmithCrop.jpg?itok=zHVVOUc7) + +# Biography[English] +Melissa Smith is a Senior Lecturer in the Department of Statistics and Data Science. She joined the faculty in January 2013. She received an A.B. in Mathematics from Dartmouth College. She completed a PhD in Biostatistics at the University of North Carolina at Chapel Hill in 1996. Her previous positions were at the Indiana University School of Medicine in the Department of Biostatistics, the Marion County Health Department (Indianapolis) and the Cornell Statistical Consulting Unit (CSCU). She works in applied statistics on problems in medicine, public health and health policy. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Michael-Nussbaum-.md b/Data/CU-DSS/CU-DSS-Michael-Nussbaum-.md new file mode 100644 index 0000000..90af1b5 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Michael-Nussbaum-.md @@ -0,0 +1,127 @@ +--- +bio-current: + name-cn: + name_en: Michael Nussbaum + email: + - hall@math.cornell.edu + - mn66@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Mathematics[https://math.cornell.edu/] + major: Mathematics + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://math.cornell.edu/michael-nussbaum # 如果有多个主页，请都填写上 + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person #没有结果 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Michael Nussbaum ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/michael-nussbaum_crop.jpg?itok=DWnvgI00) + +# Biography[English] +Michael Nussbaum is a professor of mathematics. His research program focuses on asymptotic methods in quantum statistics, in particular on hypothesis testing and discrimination between states of quantum systems. Further topics are the equivalence theory of statistical experiments, asymptotic inference in locally stationary time series and adaptive nonparametric hypothesis testing. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- Grama, **Michael Nussbaum** and Nussbaum, M., **Asymptotic equivalence for nonparametric regression.** Mathematical Methods of Statistics 11 (1) 1-36 (2002) +- Grama, **Michael Nussbaum** and Nussbaum, M., **A functional Hungarian construction for sums of independent random variables.** Annales de l'Institut Henri Poincare, Probabilites et Statistiques, 38 (6) pp. 923-957 (2002) pdf Based on the preprint A nonstandard Hungarian construction for partial sums. WIAS-preprint No. 324, 1997, Weierstrass Institute, Berlin +- Jaehnisch, M. and **Nussbaum, M.**, **Asymptotic equivalence for a model of independent non identically distributed observations.** Statistics & Decisions 21 197-218 (2003) +- **Nussbaum, M.**, **Equivalence asymptotique des experiences statistiques.** (Survey paper in French). Journal de la Societe francaise de Statistique 145 (1) 31-45 (2004) +- Jaehnisch, M. and **Nussbaum, M.**, **A functional Hungarian construction for the sequential empirical process**, C.R. Acad. Sci. Paris, Ser. I 341 761-763 (2005) +- Audenaert, K. M. R., **Nussbaum, M.**, Szkoła, A. and Verstraete, F., **Asymptotic error rates in quantum hypothesis testing.** Commun. Math. Phys. 279 (1) 251-283 (2008) +- **Nussbaum, M.** and Szkoła, A., **The Chernoff lower bound for symmetric quantum hypothesis testing.** Ann. Statist. 37 (2) 1040-1057 (2009) +- Golubev, G. K., **Nussbaum, M.** and Zhou, H. H., **Asymptotic equivalence of spectral density estimation and Gaussian white noise**, Ann. Statist. 38 (1) 181-214 (2010) +- **Nussbaum, M.** and Szkoła, A., **Exponential error rates in multiple state discrimination on a quantum spin chain**, J. Math. Phys. 51 072203 (2010) +- **Nussbaum, M.** and Szkoła, A., **Asymptotically optimal discrimination between multiple pure quantum states.** Submitted. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Nicholas-Kiefer-.md b/Data/CU-DSS/CU-DSS-Nicholas-Kiefer-.md new file mode 100644 index 0000000..f026cc8 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Nicholas-Kiefer-.md @@ -0,0 +1,172 @@ +--- +bio-current: + name-cn: + name_en: Nicholas Kiefer + email: + - nicholas.kiefer@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Economics and Statistical Science[https://economics.cornell.edu/] + major: + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://http//www.arts.cornell.edu/econ/kiefer + - https://kiefer.economics.cornell.edu/ + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/nicholas-m-kiefer/5406b2bddabfae8faa61b567 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Princeton University + school: + email: + date-start: + date-end: 1976 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: rinceton University + school: + date-start: + date-end: 1976 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Florida State University + school: + major: + date-start: + date-end: 1972 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Nicholas Kiefer ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Nicholas%20Kiefer_crop.jpg?itok=qVIs-_3g) + +# Biography[English] +Nicholas M. Kiefer works primarily in econometrics and statistics with applications in financial economics, credit scoring and risk management in banking, consumer trend forecasting, and development of quantitative management techniques. Previously, Kiefer worked on developing structural job search models and subsequently equilibrium search models. His work on the value of information, using a dynamic programming framework, led to results on the possibility and potential optimality of learning. Subsequent theoretical and empirical work on market microstructure led to invention of the PIN, a widely used statistic for measuring the information content of trades. Recently, Kiefer has developed a new approach to asymptotic approximations for use in testing problems in dynamic models. Most recently, Kiefer is developing methods for inference about small probabilities, with special interest in banking applications and the formal incorporation of expert information using Bayesian techniques. Details are given in the Research Summary. The unifying theme of the work is the complementary use of statistics and economic theory. Both statistical modeling and theoretical modeling are seen as tools to summarize and focus information. Theory and econometrics are treated as similar, complementary activities, not separate fields. This view is reflected in the new book with B.J. Christensen Economic Modeling and Inference. + +Professor Kiefer is in the departments of economics and statistical sciences and is a member of the graduate field faculty in economics, statistics and hospitality administration at Cornell University. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- 1996–present Ta-Chung Liu Professor of Economics. +- 1991–1996 Henry Scarborough Professor of Social Science. +- 1995–1999 Visiting Professor, Aarhus University. +- 1992–1995 Director, Center for Analytic Economics, Cornell University. +- 1985–present Professor, Department of Economics, Cornell University. +- 1980–1985 Associate Professor, Department of Economics, Cornell University. +- 1985–present Member, Graduate Field of Statistics, Cornell University. +- 1993–present Member, Graduate Field of Hotel Administration, Cornell University. +- 1979–1986 Research Associate, Economic Research Center, National Opinion Research Center, University of Chicago. +- 1978–1979 Senior Study Director, National Opinion Research Center, University of Chicago. +- 1978–1979 Research Associate, CORE, Universite Catholique de Louvain. +- 1976–1980 Assistant Professor, Employer, Department of Economics, University of Chicago. +- 1976–1978 NIMH Postdoctoral Fellow. + +# Work experience[中文] + +# Publication[English] +- H. Bunzel, **N.M. Kiefer**, and D.T. Mortensen. **Panel Data and Structural Labour Market Models.** Emerald Group Publishing Limited, 2000. +- Theresa J. Devine and **Nicholas M. Kiefer**. **Empirical Labor Economics: The Search Approach.** Oxford University Press, 1991. +- **Nicholas M. Kiefer.** **Economic Benefits from Four Manpower Training Programs.** Garland Series of Outstanding Dissertations in Economics. Garland Press, New York, 1979. +- **Nicholas M. Kiefer.** **Econometric Analysis of Duration Data. Journal of Econometrics-Annals.** 1985. +- **Nicholas M. Kiefer.** **The Microeconometrics of Dynamic Decision Making.** special issue Journal of Applied Econometrics. 1995. +- **Nicholas M. Kiefer** and George R. Neumann. **Search Models and Applied Labor Economics.** Cambridge University Press, 2006. +- Audra J. Bowlus, **Nicholas M. Kiefer**, and George R. Neumann. **Estimation of equilibrium wage distributions with heterogeneity.** Journal of Applied Econometrics, 10(Special Issue: The Microeconometrics of Dynamic Decision Making):S119–S131, Dec. 1995. +- Audra J. Bowlus, **Nicholas M. Kiefer**, and George R. Neumann. **Equilibrium search models and the transition from school to work.** International Economic Review, 42(2):317–343, 2001. +- Reuven Brenner and **Nicholas M. Kiefer**. **The economics of the diaspora: Discrimination and occupational structure.** Economic Development and Cultural Change, 29(3):517–534, Apr. 1981. +- Helle Bunzel, **Nicholas M. Kiefer**, and Timothy J. Vogelsang. **Simple robust testing of hypotheses in nonlinear models.** Journal of the American Statistical Association, 96(455):1088–1096, 2001. +- Kenneth Burdett, **Nicholas M. Kiefer**, Dale T. Mortensen, and George R. Neumann. **Earnings, unemployment, and the allocation of time over time**. The Review of Economic Studies, 51(4):559–578, Oct. 1984. +- Hwansik Choi and **Nicholas M. Kiefer**. **Improving robust model selection tests for dynamic models.** Econometrics Journal, 13(2):177–204, 2010. +- Bent Jesper Christensen and **Nicholas M. Kiefer.** **The exact likelihood function for an empirical job search model.** Econometric Theory, 7(4):464–486, 1991. +- Bent Jesper Christensen and **Nicholas M. Kiefer.** **Measurement error in the prototypal job-search model.** Journal of Labor Economics, 12(4):618–639, Oct. 1994. +- Bent Jesper Christensen and **Nicholas M. Kiefer.** **Panel data, local cuts and orthogeodesic models.** Bernoulli, 6(4):667–678, Aug. 2000. +- Theresa J. Devine and **Nicholas M. Kiefer.** **The empirical status of job search theory.** Labour Economics, 1(1):3 – 24, 1993. +- David Easley and **Nicholas M. Kiefer.** **Controlling a stochastic process with unknown parameters.** Econometrica: Journal of the Econometric Society, 56(5):1045–1064, Sep. 1988. +- David Easley and **Nicholas M. Kiefer.** **Optimal learning with endogenous data.** International Economic Review, 30(4):963–978, Nov. 1989. +- David Easley, **Nicholas M. Kiefer**, and Maureen O’Hara. **Cream-skimming or profit-sharing? the curious role of purchased order flow.** The Journal of Finance, 51(3):811–833, 1996. +- David Easley, **Nicholas M. Kiefer**, and Maureen O’Hara. **One day in the life of a very common stock.** The Review of Financial Studies, 10(3):805–835, 1997. +- David Easley, **Nicholas M. Kiefer**, Maureen O’Hara, and Joseph B. Paperman. **Liquidity, information, and infrequently traded stocks.** The Journal of Finance, 51(4):1405–1436, 1996. +- David Easley, **Nicholas M. Kiefer**, and Uri Possen. **An equilibrium analysis of optimal unemployment insurance and taxation.** The Quarterly Journal of Economics, 100(Supplement):989–1010, 1985. +- David Easley, **Nicholas M. Kiefer**, and Uri M. Possen. **An equilibrium analysis of fiscal policy with uncertainty and incomplete markets.** International Economic Review, 34(4):935–952, Nov. 1993. +- D. Glennon, C. E. Larson, **Nicholas Kiefer**, and H. Choi. **Development and validation of credit-scoring models.** Journal of Credit Risk, 4:1–61, 2008. +- J. Gomez-Gonzalez and **Nicholas M Kiefer**. **Evidence of non-markovian behavior in the process of bank rating migrations.** Cuadernos de Economía, 46:33–50, 2009. +- Jose Gomez-Gonzalez and **Nicholas M Kiefer**. **Bank failure: Evidence from the colombian financial crisis.** The International Journal of Business and Finance Research, 3:15–31, 2009. +- **Nicholas M. Kiefer.** Quadratic utility, labor supply and commodity demand. **Studies in Nonlinear Estimation**, pages 167–169, 1976. +- **Nicholas M. Kiefer**. **A bayesian analysis of commodity demand and labor supply.** International Economic Review, 18(1):209–218, Feb. 1977. +- **Nicholas M. Kiefer**. **Discrete parameter variation: Efficient estimation of a switching regression model.** Econometrica: Journal of the Econometric Society, 46(2):427–434, Mar. 1978. +- **Nicholas M. Kiefer**. **Estimating mixtures of normal distributions and switching regressions: Comment.** Journal of the American Statistical Association, 73(364):744–745, Dec. 1978. +- **Nicholas M. Kiefer.** **Federally subsidized occupational training and the employment and earnings of male trainees.** Journal of Econometrics, 8:111–125, 1978. +- **Nicholas M. Kiefer.** **On the value of sample separation information.** Econometrica, 47(4):997–1003, 1979. +- **Nicholas M. Kiefer.** **Population heterogeneity and inference from panel data on the effects of vocational education.** The Journal of Political Economy, 87(5, Part 2: Education and Income Distribution):S213–S226, Oct. 1979. +- **Nicholas M. Kiefer.** **Estimation of fixed effects models for time series of cross-sections with arbitrary intertemporal covariance.** Journal of Econometrics, 14:195–202, 1980. +- **Nicholas M. Kiefer.** **A note on switching regressions and logistic discrimination.** Econometrica: Journal of the Econometric Society, 48(4):1065–1069, May 1980. +- **Nicholas M. Kiefer.** **Limited information analysis of a small underidentified macroeconomic model.** International Economic Review, 22(2):429–442, Jun. 1981. +- **Nicholas M. Kiefer.** **Identifying restrictions in limited information analysis of the schooling coefficient in a wage regression.** Journal of Econometrics, 13:219–237, 1982. +- **Nicholas M. Kiefer.** **Testing for dependence in multivariate probit models.** Biometrika, 69(1):161–166, Apr. 1982. +- **Nicholas M. Kiefer.** **Methods for analyzing employment contracts and other agreements**, presented at the 1984 asa meetings. Proceedings of the ASA Business and Economic Statistics Section, pages 103–106, 1984. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Sumanta-Basu-.md b/Data/CU-DSS/CU-DSS-Sumanta-Basu-.md new file mode 100644 index 0000000..67c25c0 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Sumanta-Basu-.md @@ -0,0 +1,146 @@ +--- +bio-current: + name-cn: + name_en: Sumanta Basu + email: + - sb2457@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - Department of Computational Biology[https://compbio.cornell.edu/] + major: + title-raw: Associate Professor + title: Associate Professor + interests: + - developing statistical machine learning methods for structure learning and prediction of complex + - high-dimensional systems arsing in biological and social science + homepage: + - http://faculty.bscb.cornell.edu/~basu/ + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Michigan + school: + - Department of Statistics[http://lsa.umich.edu/stats/] + email: + date-start: + date-end: 2014 + advisor: # 格式：导师名 [邮箱/网址] + degree: phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: Indian Statistical Institute + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Indian Statistical Institute + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.researchgate.net/profile/Sumanta-Basu-2 + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![**Sumanta Basu** ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/basu%20sumanta.jpg?itok=A8fvwHje) + +# Biography[English] +**Sumanta Basu** is broadly interested in structure learning and prediction of complex, high-dimensional systems arising in biological and social sciences. His current research focuses on network modeling of high-dimensional time series and nonlinear ensemble learning methods. + +I am broadly interested in developing statistical machine learning methods for structure learning and prediction of complex, high-dimensional systems arising in biological and social sciences. I am currently working in two areas: (a) network modeling of high-dimensional time series; and (b) detecting high-order interactions in complex biological systems using randomized tree ensembles. I also work closely with scientists and economists on a wide range of problems including prostate cancer progression, large scale metabolomics, and systemic risk monitoring in financial markets. + +My research is supported in part by a three-year grant from the National Science Foundation (NSF DMS-1812128), a four-year grant (1R01GM135926-01) from Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences (DMS/NIGMS), and a two-year grant (1R21NS120227-01) from the National Institute of Health (NIH). + +Before joining Cornell, I was a postdoctoral scholar (2014-2016) in the Department of Statistics, UC Berkeley and the Biosciences Division, Lawrence Berkeley National Laboratory . I received my PhD (2014) from the Department of Statistics, University of Michigan , and my bachelors (2006) and masters (2008) in Statistics from Indian Statistical Institute, Kolkata . Before joining the PhD program, I worked for a year as a business analyst at Wipro Technologies . + + + +# Biography[中文] + +# Interests[English] +- developing statistical machine learning methods for structure learning and prediction of complex +- high-dimensional systems arsing in biological and social science + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- postdoctoral scholar (2014-2016) in the Department of Statistics, UC Berkeley and the Biosciences Division, Lawrence Berkeley National Laboratory +- (2014-present)Associate Professor, Cornell University. +- Before joining the PhD program, worked for a year as a business analyst at Wipro Technologies . +# Work experience[中文] + +# Publication[English] +- **Methionine-Homocysteine Pathway in African-American Prostate Cancer**, Article, Apr 2019, Jie H Gohlke, Stacy Lloyd, **Sumanta Basu**, Arun Sreekumar +- **Abstract B92: Metabolomic landscape of African American prostate cancer: Insights into the biologic basis of the racial disparity**, Conference Paper, Jul 2018, Stacy Lloyd, Jie H. Gohlke, **Sumanta Basu**, Arun Sreekumar +- **Sparse network modeling and Metscape-based visualization methods for the analysis of large-scale metabolomics data**, Article, Jan 2017, **Sumanta Basu**, William Duren, Charles Evans, Alla Karnovsky +- **Measuring Systemic Risk with Network Connectivity: Extended Abstract**, Conference Paper, Jun 2016, **Sumanta Basu**, Sreyoshi Das, George Michailidis, +- **Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models**, Article, Aug 2016, Jiahe Lin, **Sumanta Basu**, Moulinath Banerjee, +- **Regularized estimation in sparse high-dimensional time series models**, Article, Aug 2015, **Sumanta Basu**, George Michailidis, , +- **Abstract A35: Metabolomic profiling and the biochemical basis of prostate cancer racial disparity.**, Article, Mar 2014, Shaiju Kakkandan Vareed, Katrin Panzitt, **Sumanta Basu**, Arun Sreekumar +- **Modeling and Estimation of High-dimensional Vector Autoregressions.**, Article, Jan 2014, **Sumanta Basu** +- **Metabolomic Profiling Identifies Biochemical Pathways Associated with Castration-Resistant Prostate Cancer**, Article, Dec 2013, Akash Kaushik, Shaiju K Vareed, **Sumanta Basu**, Arun Sreekumar +- **Estimation in High-dimensional Vector Autoregressive Models**, Article, Nov 2013, **Sumanta Basu**, George Michailidis, , +- **Abstract 5387: Integrative analysis of transcriptomic and metabolomic data reveals a critical role for aminosugar metabolism in prostate cancer**, Article, Aug 2013, Katrin Panzitt, Ali Shojaie, Nagireddy Putluri, Arun Sreekumar +- **Network Granger Causality with Inherent Grouping Structure**, Article, Oct 2012, **Sumanta Basu**, Ali Shojaie, George Michailidis, +- **Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data**, Article, May 2011, Ali Shojaie, **Sumanta Basu**, George Michailidis, + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Thomas-DiCiccio-.md b/Data/CU-DSS/CU-DSS-Thomas-DiCiccio-.md new file mode 100644 index 0000000..0bdde1c --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Thomas-DiCiccio-.md @@ -0,0 +1,176 @@ +--- +bio-current: + name-cn: + name_en: Thomas DiCiccio + email: + - tjd9@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + - Department of ILR[https://www.ilr.cornell.edu/] + major: statistical methodology + title-raw: Associate Professor + title: Associate Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://www.ilr.cornell.edu/people/thomas-diciccio + github: + googlescholar: + aminer: + - https://www.aminer.cn/profile/thomas-j-diciccio/53f432fadabfaeb22f44e702 #个人信息尚未更新 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.researchgate.net/scientific-contributions/Thomas-J-DiCiccio-5841420 + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![**Thomas DiCiccio** ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/diciccio.jpg?itok=ZAWAzVe0) + +# Biography[English] +Tom DiCiccio is an Associate Professor of Social Statistics in the Industrial and Labor Relations School and Director of Undergraduate Studies. His research includes the following areas: Econometrics, Theory and models and Statistical Theory and Methodology. Professor DiCiccio teaches courses within the ILR School and the Department of Statistics and Data Science. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] + +# Work experience[中文] + +# Publication[English] +- Mallika Banerjee, Pamela Tolbert, ****Thomas DiCiccio****. 2012. **Friend or foe? The effects of contingent workers on standard workers' job sttitudes**, International Journal of Human Resources Management . 11:2180-2204. +- **Confidence Intervals for Seroprevalence**, Preprint Mar 2021, ****Thomas J. DiCiccio**** David Ritzwoller Joseph Romano +- **The formal relationship between analytic and bootstrap approaches to parametric inference**, Article Jun 2017, **T.J. DiCiccio** Todd Kuffner G.A. Young +- **A Simple Analysis of the Exact Probability Matching Prior in the Location-Scale Model**, Article Aug 2016, **Thomas J Diciccio** Todd Kuffner G Alastair Young +- **Stability and uniqueness of $p$-values for likelihood-based inference**, Article Mar 2015, **Thomas J. DiCiccio** Todd Kuffner G. Alastair Young +- **Quantifying nuisance parameter effects via decompositions of asymptotic refinements for likelihood-based statistics**, Article Mar 2015, **Thomas J. DiCiccio** Todd Kuffner G. Alastair Young +- **Robust portfolio estimation under skew-normal return processes**, Article Dec 2012, Masanobu Taniguchi Alexandre Petkovic Takehiro Kase **Thomas J. DiCiccio** +- **Objective Bayes, conditional inference and the signed root likelihood ratio statistic**, Article Aug 2012, **Thomas J. DiCiccio** Todd Kuffner G. Alastair Young +- **Jackknifed Whittle estimators**, Article Jul 2012, Masanobu Taniguchi Kenichiro Tamaki **Thomas J. DiCiccio** +- **Friend or Foe? The effects of contingent employees on standard employees' work attitudes**, Article Jun 2012, Mallika Banerjee Pamela S. Tolbert **Thomas DiCiccio** +- **Testing for Sub-models of the Skew t-distribution**, Article Apr 2011, **Thomas J. DiCiccio** Anna Clara Monti +- **Conditional Inference by Estimation of a Marginal Distribution**, Chapter Feb 2011, **Thomas J. DiCiccio** G. Alastair Young +- **Computer-intensive conditional inference**, Chapter Jan 2011, G. Alastair Young **Thomas J. DiCiccio** +- **Objective Bayes and conditional inference in exponential families**, Article May 2010, **Thomas J. DiCiccio** G. Alastair Young +- **The automatic percentile method: Accurate confidence limits in parametric models**, Article Dec 2008, **Thomas J. DiCiccio** Joseph Romano +- **Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families**, Article Sep 2008, **Thomas J. DiCiccio** G. Alastair Young +- **Variance stabilization for a scalar parameter**, Article Apr 2006, **Thomas J. DiCiccio** Anna Clara Monti G. Alastair Young +- **Inferential Aspects of the Skew Exponential Power Distribution**, Article Jun 2004, **Thomas J. DiCiccio** Anna Clara Monti +- **A Conversation with Donald A. S. Fraser**, Article May 2004, **Thomas J. DiCiccio** Mary E Thompson +- **Accurate confidence limits for scalar functions of vector M-estimands**, Article Jun 2002, **Thomas J. DiCiccio** +- **Approximations to the profile empirical likelihood function for a scalar parameter in the context of M-estimation**, Article Jun 2001, **Thomas J. DiCiccio** Anna Clara Monti +- **Simple and accurate one‐sided inference from signed roots of likelihood ratios**, Article Mar 2001, **Thomas J. DiCiccio** Michael A. Martin Steven E. Stern +- **On expected volumes of multidimensional confidence sets associated with the usual and adjusted likelihoods**, Article Feb 2001, Gauri S. Datta **Thomas J. DiCiccio** +- **Simple and Accurate One-Sided Inference From Signed Roots of Likelihood Ratios**, Article Apr 2000, **Thomas J. DiCiccio** +- **Computing Bayes Factors by Combining Simulation and Asymptotic Approximations**, Article Sep 1997, **Thomas J. DiCiccio** Robert Kass Adrian E. Raftery +- **Introduction to Barndorff-Nielsen (1983) On a Formula for the Distribution of the Maximum Likelihood Estimator**, Chapter Jan 1997, T. DiCiccio +- **Statistical inference and Monte Carlo algorithms**, Article Dec 1996, George Casella Juan Ferrándiz Daniel Peña +- **Bootstrap confidence intervals. With comments and a rejoinder by the authors**, Article Sep 1996, **Thomas J. DiCiccio** Bradley Efron +- **Better Bootstrap Confidence Intervals**, Article Jan 1996, T.J. DiCiccio Bradley Efron +- **Information Bias and Adjusted Profile Likelihoods**, Article Jan 1996, **Thomas J. DiCiccio** Steven E. Stern Michael A. Martin +- **[Inference Based on Estimating Functions in the Presence of Nuisance Parameters]: Comment: Alternative Aspects of Conditional Inference**, Article May 1995, George Casella **Thomas J. DiCiccio** Martin T. Wells +- **On bootstrap procedures for second-order accurate confidence limits in parametric models**, Article Jan 1995, Thomas J Diciccio Joseph Romano +- **Frequentist and Bayesian Bartlett Correction of Test Statistics Based on Adjusted Profile Likelihoods**, Article Jul 1994, **Thomas J. DiCiccio** Steven E. Stern +- **Analytic approximations to bootstrap distribution functions using saddlepoint methods**, Article Jan 1994, Michael A. Martin **Thomas J. DiCiccio** G. Alastair Young +- **Frequentist and Bayesian Bartlett correction of test statistics based on adjusted pro le likelihoods**, Article Jan 1994, T. J. Diciccio Steven E. Stern +- **Constructing approximately standard normal pivots from signed roots of adjusted likelihood ratio statistics**, Article Jan 1994, **Thomas J. DiCiccio** Steven E. Stern +- **On Bartlett Adjustments for Approximate Bayesian Inference**, Article Dec 1993, **Thomas J. DiCiccio** Steven E. Stern +- **Analytical Approximations to Conditional Distribution Functions**, Article Dec 1993, Michael A. Martin **Thomas J. DiCiccio** G. Alastair Young +- **Simple Modifications for Signed Roots of Likelihood Ratio Statistics**, Article Sep 1993, **Thomas J. DiCiccio** Michael A. Martin +- **Analytic approximations for iterated bootstrap confidence intervals**, Article Sep 1992, **Thomas J. DiCiccio** Michael A. Martin G. Alastair Young +- **Fast and Accurate Approximate Double Bootstrap Confidence Intervals**, Article Jun 1992, Michael A. Martin **Thomas J. DiCiccio** G. Alastair Young +- **More Accurate Confidence Intervals in Exponential Families**, Article Jun 1992, **Thomas DiCiccio** Bradley Efron +- **Accurate Procedures for Approximate Bayesian and Conditional Inference Without the Need for Orthogonal Parameters**, Article Feb 1992, **Thomas J. DiCiccio** Joseph B. Keller Michael A. Martin +- **Approximations of Marginal Tail Probabilities for a Class of Smooth Functions with Applications to Bayesian and Conditional Inference**, Article Dec 1991, Michael A. Martin **Thomas J. DiCiccio** +- **An Accurate Method for Approximate Conditional and Bayesian Inference about Linear Regression Models from Censored Data**, Article Dec 1991, **Thomas J. DiCiccio** Chris Field +- **An invariance property of marginal density and tail probability approximations for smooth functions**, Article Sep 1991, **Thomas J. DiCiccio** Michael A. Martin G. Alastair Young +- **Nonparametric Confidence Limits by Resampling Methods and Least Favorable Families**, Article Apr 1990, **Thomas J. DiCiccio** Joseph Romano +- **Approximations of Marginal Tail Probabilities and Inference for Scalar Parameters**, Article Mar 1990, **Thomas J. DiCiccio** Chris Field D. A. S. FRASER +- **Comparison of Parametric and Empirical Likelihood Functions**, Article Sep 1989, **Thomas J. DiCiccio** Peter Hall Joseph Romano +- **On Adjustments Based on the Signed of the Empirical Likelihood Ratio Statistic**, Article Sep 1989, **Thomas J. DiCiccio** Joseph Romano +- **A Review of Bootstrap Confidence Intervals**, Article Jul 1989, **Thomas J. DiCiccio** Joseph Romano +- **On Smoothing and the Bootstrap, Article Jun 1989**, Peter Hall **Thomas J. DiCiccio** Joseph Romano +- **Discussion: Theoretical Comparison of Bootstrap Confidence Intervals**, Article Sep 1988, **Thomas J. DiCiccio** Joseph Romano +- **Better Bootstrap Confidence Intervals: Comment**, Article Mar 1987, **Thomas DiCiccio** Robert Tibshirani +- **Bootstrap Confidence Intervals and Bootstrap Approximations**, Article Mar 1987, **Thomas DiCiccio** Robert Tibshirani +- **Approximate Inference for the Generalized Gamma Distribution**, Article Feb 1987, **T. J. DiCiccio** +- **Approximate conditional inference for location familles**, Article Mar 1986, **Thomas J. DiCiccio** + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Xiaolong-Yang-.md b/Data/CU-DSS/CU-DSS-Xiaolong-Yang-.md new file mode 100644 index 0000000..745adba --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Xiaolong-Yang-.md @@ -0,0 +1,137 @@ +--- +bio-current: + name-cn: + name_en: Xiaolong Yang + email: + - xy44@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Senior Lecture and Senior Associate Director + title: # Associate Professor/Assistant Professor/Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - # 如果有多个主页，请都填写上 + github: + googlescholar: + aminer: # 从这里查找 https://www.aminer.org/search/person #无 + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://www.researchgate.net/profile/Xiaolong-Yang-11 + research: + software: + project: + blog: + arxiv: + - https://arxiv.org/search/math?searchtype=author&query=Yang%2C+X + linkedin: + - https://www.linkedin.com/in/xiaolong-yang-a97bb01b/ + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![**Xiaolong Yang** ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/xiaolong-yang_crop.jpg?itok=ZF_W6atF) + +# Biography[English] +Sr. Associate Director, MPS Program, Department of Statistics and Data Science, Cornell University +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Faculty, Cornell, 2006-present +# Work experience[中文] + +# Publication[English] +- **A Comparison of Protein Extraction Methods Suitable for Gel-Based Proteomic Studies of Aphid Proteins**, Article, Full-text available, Oct 2009, Michelle Cilia Tara Fish **Xiaolong Yang** S Gray +- **Protein N- and C-termini identification using mass spectrometry and isotopic labeling**, Article, Jul 2009, Bosong Xiang **Xiaolong Yang** Theodore W Thannhauser +- **Coupling Genetics and Proteomics To Identify Aphid Proteins Associated with Vector-Specific Transmission of Polerovirus (Luteoviridae)**, Article, Full-text available, Feb 2008, **Xiaolong Yang** Theodore W Thannhauser Mary Burrows Stewart M Gray +- **Additional File 1**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 3**, Data, Full-text available, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 4**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 5**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 2**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 6**, Data, Full-text available, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 7**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 8**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Additional File 9**, Data, May 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **Effects of parasitization by Varroa destructor on survivorship and physiological traits of Apis mellifera in correlation with viral incidence and microbial challenge**, Article, Apr 2007, **Xiaolong Yang** Diana Cox-Foster +- **P192-T On-Slide Chemical Modification as a Means to Improve Confidence in Protein Identifications Made by Peptide Mass Fingerprints**, Article, Full-text available, Feb 2007, **Xiaolong Yang** Theodore W Thannhauser +- **Expansion and evolution of insect GMC oxidoreductases, Article, Full-text available**, Feb 2007, Kaori Iida Diana Cox-Foster **Xiaolong Yang** Douglas R Cavener +- **In search of proteins in the aphid Schizaphis graminum associated with vectoring luteoviruses: A proteomics approach**, Article, Jun 2006, **Xiaolong Yang** Mary Burrows Theodore W Thannhauser F Gildow +- **The role of Varroa mites in infections of Kashmir bee virus (KBV) and deformed wing virus (DWV) in honey bees**, Article, Dec 2005, Miaoqing Shen **Xiaolong Yang** Diana Cox-Foster +- **Impact of an ectoparasite on the immunity and pathology of an invertebrate: Evidence for host immunosuppression and viral amplification**, Article, Full-text available, Jun 2005, **Xiaolong Yang** Diana Cox-Foster +- **Effects of varroa mites on the immune system and pathology of honey bees**, Article, **Xiaolong Yang** + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Y.-Samuel-Wang-.md b/Data/CU-DSS/CU-DSS-Y.-Samuel-Wang-.md new file mode 100644 index 0000000..46d50a6 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Y.-Samuel-Wang-.md @@ -0,0 +1,159 @@ +--- +bio-current: + name-cn: + name_en: Y. Samuel Wang + email: + - ysw7@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Associate Professor + title: Associste Professor + interests: + - Causal Discovery + - Probabilistic Graphical Models + - High-Dimensional Statistics + - Machine Learning + homepage: + - https://ysamuelwang.com/ # 如果有多个主页，请都填写上 + github: + - https://github.com/ysamwang + googlescholar: + - https://scholar.google.com/citations?user=ih_LeQYAAAAJ&hl=en6 + aminer: # 从这里查找 https://www.aminer.org/search/person + status: # 选项如下：在读/在职/离职/退休/亡故 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: University of Washington + school: Depatment of Statistics[https://stat.uw.edu/] + email: + date-start: 2012 + date-end: 2018 + advisor: Mathias Drton[md5@uw.edu] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: Rice University + school: + - Department of Mathematics[https://math.rice.edu/] + - Department of Economics[https://economics.rice.edu/] + major: + - Applied Math + - Economics + date-start: 2006 + date-end: 2010 +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + - https://ysamuelwang.com/publications/ + research: + software: + project: + blog: + arxiv: + linkedin: + - https://www.linkedin.com/in/ysamuelwang/ + weibo: + twitter: + - https://twitter.com/ysamuelwang + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Y. Samuel Wang ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/wang%20y%20samuel.jpg?itok=TSUrORO6) + +# Biography[English] +Y. Samuel (Sam) Wang joins the Department of Statistics and Data Science as an assistant professor in Fall 2021. Previously, he was a post-doc at the University of Chicago’s Booth School of Business, and he completed his PhD in Statistics at the University of Washington. He also previously worked as a management consultant before his PhD studies. His primary research areas are graphical models and causal discovery. + +I am currently an assistant professor at Cornell in the Department of Statistics and Data Science. I was previously a principal researcher (post-doc) at the University of Chicago’s Booth School of Business working with Mladen Kolar, and I completed my PhD in Statistics at the University of Washington under the supervision of Mathias Drton. + +I enjoy thinking about problems where the goal is to discover interpretable structure which underlies the data generating process. This includes problems in the areas of causal discovery, graphical models, and mixed membership models. In many cases, the methods are tailored for the high-dimensional setting where the number of variables considered may be large when compared to the number of observed samples. My applied interests vary, but are generally social science related. + +I received my undergraduate degree in both applied math and economics at Rice University, and I worked as a management consultant for two years before embarking on my PhD studies. In my free time, I enjoy playing soccer, attempting to cook, riding my bike, and stressing about the Dallas Cowboys. + +# Biography[中文] + +# Interests[English] +- Causal Discovery +- Probabilistic Graphical Models +- High-Dimensional Statistics +- Machine Learning +# Interests[中文] + +# Education[English] +- Ph.D. in Statistics 2012 - 2018, University of Washington, Thesis: Linear structural equation models with non-Gaussian errors, Advisor: Mathias Drton +- Committee members: Thomas Richardson and Emily Fox,B.A. in Applied Math; Economics 2006 - 2010, Rice University, Magna Cum Laude; Phi Beta Kappa + +# Education[中文] + +# Awards[English] +- **“Confidence sets for causal discovery”**, with Mathias Drton and Mladen Kolar +- **“Posterior summarization for time varying dynamic Bayesian models”**, with Mladen Kolar, Si Kai Lee, and David Puelz +- **“Estimation of functional graphical models via neighborhood selection”**, with Mladen Kolar, Percy Zhai, and Boxin Zhao +- **“Non-parametric estimation of the score function”**, with Mladen Kolar + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Assistant Professor, Cornell University, 2021.8-present +- Postdoctoral Researcher, University of Chicago, 2018.5-2021.7 +- Assistant Trader Intern, Susquehanna International Group, LLP(SIG), 2013,less than 1 year +- Strategy and Operations Consultant, Deloitte, 2010.7-2012.9 + +# Work experience[中文] + +# Publication[English] +- **Gender-based homophily in collaborations across a heterogeneous scholarly landscape**, Submitted, 2020+ , **Wang, Y. S.**, Lee, C. J., West, J. D., Bergstrom, C. T., Erosheva, E. A. +- **FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves**, Submitted, 2020+ , Zhao, B., **Wang, Y. S.**, Kolar, M. +- **Causal discovery with unobserved confounding and non- Gaussian data**, Submitted, 2020+ , **Wang, Y. S.**, Drton, M. +- **Robust Inference for High-Dimensional Linear Models via Residual Randomization**, ICML, 2021 , **Wang, Y. S.**, Lee, S.K., Toulis, P., Kolar, M. +- **High-dimensional causal discovery under non-Gaussianity**, Biometrika, 2020 , **Wang, Y. S.**, Drton, M. +- **On causal discovery with an equal-variance assumption**, Biometrika, 2019 , Chen, W., Drton, M., **Wang, Y. S.** +- **Direct estimation of differential functional graphical models**, NeurIPS, 2019 , Zhao, B., **Wang, Y. S.**, Kolar, M. +- **Computation of maximum likelihood estimates in cyclic structural equation models**, Annals of Statistics, 2019 , Drton, M., Fox, C., **Wang, Y. S.** +- **On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example**, Annals of Applied Statistics, 2018 , Chen, Y. C., **Wang, Y. S.**, Erosheva, E. A. +- **A variational EM method for mixed membership models with multivariate rank data: An analysis of public policy preferences**, Annals of Applied Statistics, 2017 , **Wang, Y. S.**, Matsueda R., Erosheva, E. A. +- **Empirical likelihood for linear structural equation models with dependent errors**, Stat, 2017 , **Wang, Y. S.**, Drton, M. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Yang-Ning-.md b/Data/CU-DSS/CU-DSS-Yang-Ning-.md new file mode 100644 index 0000000..56df5d8 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Yang-Ning-.md @@ -0,0 +1,139 @@ +--- +bio-current: + name-cn: + name_en: Yang Ning + email: + - yn265@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Statistics and Data Science[https://stat.cornell.edu/] + major: + title-raw: Assistant Professor + title: Assistant Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - http://yangning.stat.cornell.edu/ # 如果有多个主页，请都填写上 + github: + googlescholar: + - https://scholar.google.com/citations?user=3xcQVXsAAAAJ&sortby=pubdate + aminer: # 从这里查找 https://www.aminer.org/search/person #无 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: Johns Hopkins University + school: + - Bloomberg School of Public Health[https://publichealth.jhu.edu/departments/biostatistics] + email: + date-start: 2007 + date-end: + advisor: + - Hongkai Ji[hji@jhu.edu] + - Kung-Yee Liang[https://www.linkedin.com/in/kung-yee-liang-1607157/] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: Fudan University(China) + school: School of Mathematical Science[https://math.fudan.edu.cn/] + date-start: + date-end: 2012 + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc-1: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: Princeton University + school: + email: + date-start: 2014 + date-end: 2016 + advisor: Han Liu[hanliu@eecs.northwestern.edu] +job-post-doc-2: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: University of Toronto and University of Waterloo + school: + email: + date-start: 2012 + date-end: 2014 + advisor: + - Nancy Reid[reid@utstat.toronto.edu] + - Grace Yi[yyi@uwaterloo.ca] +--- + +# Profile + +![Yang Ning ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Yang-Ning-edit.jpg?itok=PuAOkZ3B) + +# Biography[English] +Yang Ning comes to Cornell as an Assistant Professor after completing a postdoc fellowship at Princeton University, where he developed statistical methods and theory for analyzing big and complex data. He has been awarded the David Byar Young Investigator Award and has his papers published in Biometrika and the Journal of the Royal Statistical Society. + +Ning obtained a Bachelor's degree in Mathematics from Fudan University, China, and a Ph.D degree in Biostatistics from the Johns Hopkins University. The theme of his research is to develop statistical methods and theory to quantify the uncertainty (confidence interval and hypothesis test) in modern data sets, which are characterized by high dimensionality, complexity and heterogeneity. He enjoys working at the interface of mathematical statistics, machine learning and stochastic optimization. He is also interested in applied projects in genomics, Neuroscience, epidemiology and clinical trials. + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] +- Ph.D. in Biostatistics, Johns Hopkins University, 2012,Advisor: Kung-Yee Liang and Hongkai Ji +- B.S. in Mathematics, Fudan University, China, 2007 + +# Education[中文] + +# Awards[English] +- 2012 David P. Byar Young Investigator Award +- 2012 ENAR Distinguished Student Paper Award +- 2016 IMS Travel Award +- 2019 Elected member of the International Statistical Institute +- 2020 NSF CAREER Award + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- Assistant Professor, Department of Statistics and Data Science, Cornell University, 2016–present + +# Work experience[中文] + +# Publication[English] + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS-Yongmiao-Hong-.md b/Data/CU-DSS/CU-DSS-Yongmiao-Hong-.md new file mode 100644 index 0000000..620e869 --- /dev/null +++ b/Data/CU-DSS/CU-DSS-Yongmiao-Hong-.md @@ -0,0 +1,172 @@ +--- +bio-current: + name-cn: + name_en: Yongmiao Hong + email: + - yh20@cornell.edu + sex: male + birth-date: # yyyy 到年即可 + university: Cornell University + school: + - Department of Economics[https://economics.cornell.edu/] + major: Economics + title-raw: Professor + title: Professor + interests: # 分点罗列，依次以 ‘-’ 开头 + homepage: + - https://economics.cornell.edu/yongmiao-hong + github: + googlescholar: + - https://scholar.google.com/citations?user=HnMSuCkAAAAJ + aminer: + - https://www.aminer.cn/profile/yongmiao-hong/542a9338dabfae646d5734e5 + status: 在职 + last-update: # yyyy-mm-dd 最近一次信息更新日期 +edu-phd: # 读博经历 + university: + school: + email: + date-start: + date-end: + advisor: # 格式：导师名 [邮箱/网址] + degree: # phd +edu-master: # 硕士经历，没有或找不到，可不填 + university: + school: + date-start: + date-end: + advisor: +edu-bachelor: # 本科经历，没有或找不到，可不填 + university: + school: + major: + date-start: + date-end: +page-other: # 其他有用的链接，部分可从学者主页子栏目获得 + publication: + research: + software: + project: + blog: + arxiv: + linkedin: + weibo: + twitter: + wikipedia: + baidu-baike: + - https://baike.baidu.com/item/%E6%B4%AA%E6%B0%B8%E6%B7%BC/5460119?fr=aladdin +collaboration: # 合作研究，关注学者和其他哪些学科的人合作，具体研究哪些主题 + - + with: # 合作者 + project: # 研究主题 + - + with: + project: +group: # 所属团队，学者可能有不同的兴趣小组，可以列上去 +job-faculty-1: # 所属机构，若有多个增加编号即可，字段填写参看示例文件 + university: + school: + major: + email: + homepage: # 机构内学者主页 + date-start: + title: + type: +job-post-doc: # 博士后研究员，字段填写参看示例文件，若无可不填写 + university: + school: + email: + date-start: + date-end: + advisor: +--- + +# Profile + +![Yongmiao Hong ](https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/yhong.jpg?itok=Ng4C98lQ) + +# Biography[English] +Professor Hong's research interests include model specification testing, nonlinear time series analysis, generalized spectral analysis, econometrics of time-varying models, financial econometrics, interval-valued time series econometrics, and evaluation of Chinese economic policies. + +On specification testing, Professor Hong develops a class of sophisticated semiparametric tests for econometric models of cross-sectional, time series, and panel data respectively. The basic idea is to compare a null econometric model, with a flexible nonparametric alternative, developing tests that have power against various model misspecifications. Nonparametric tools used include orthogonal series, kernel, and wavelet methods. In Hong and White (1995, Econometrica), a generalized F test is developed by comparing the sums of squared residuals of a parametric regression model and a nonparametric series regression model where the order of the series expansion grows, with the sample size, ensuring the test able to detect various functional form misspecifications. This methodology is extended in Hong (1996, Econometrica) to test a dynamic regression model. This is achieved by comparing a nonparametric kernel estimator for the spectral density of the estimated model residual, with the flat spectrum of a serially uncorrelated white noise. An optimal kernel function or weighting function for lags is derived to ensure that the proposed test has optimal power. This test is generalized in Hong and Kao (2004, Econometrica) to a panel data regression model where wavelets are used in nonparametric spectral density estimation. Hong and Lee (2013, Annals of Statistics) show that a loss function-based specification testing approach is asymptotically more efficient than a generalized likelihood ratio test approach which includes the tests based on comparing sums of squared residuals. + +Another main area of Professor Hong's research interests is nonlinear time series analysis, locally stationary time series analysis, and generalized spectral analysis. Observing that many economic and financial time series are serially uncorrelated but not serially independent, Hong (1998, Journal of Royal Statistical Society, Series B; 2000, Journal of Rayal Statistical Society, Series B) develops nonparametric Hoeffding-type measures of and tests for serial dependence in a time series which can detect subtle dependence structure. In particular, Hong and White (2005, Econometrica) develop a challenging asymptotic distribution theory for smoothed nonparametric entropy measures of serial dependence which was not previously available in the literature. Chen and Hong (2012, Econometrica) propose a new approach to testing parameter constancy of a time series regression model against smooth structural changes as well as abrupt structural breaks. + +On another important development, Hong (1999, Journal of American Statistical Association) proposes a new analytic tool for nonlinear economic time series -- the generalized spectrum. The basic idea is to transform original time series data via a complex-valued exponential function and then consider the spectrum of the transformed series. This can capture both linear and nonlinear serial dependence while avoiding the drawbacks of the conventional spectrum, which cannot capture nonlinear serial dependence, and higher order spectra (e.g., bispectrum), which requires the existence of restrictive moment conditions. Real data applications (e.g., Hong and Lee (2003a, Review of Economics and Statistics)) show that the generalized spectral tool can detect dynamic structures which would otherwise be neglected by conventional tools, thus offering new insights into economic and financial time series data. The generalized spectrum is also used to develop powerful procedures for nonlinear time series analysis. For example, Hong and Lee (2003b, Econometric Theory) use it to check any neglected dependence structure in the estimated standardized residuals of a nonlinear time series model, and Hong and Lee (2005, Review of Economic Studies) use the first order partial derivative of the generalized spectrum to focus on neglected nonlinearity in the conditional mean dynamics of a time series model. Hong and Lee (2003b, Econometric Theory) win the Koopman Econometrics Prize 2006. + +Hong also works on financial econometrics. Hong and Li (2005, Review of Financial Studies) develop a nonparametric specification test for continuous-time models using discretely sampled data. The basic idea is to consider transformed data via the model-implied dynamic transition density, which should be independent and uniformly distributed if the continuous-time model is correctly specified. The proposed test is generally applicable and robust to persistent dependence in data because the i.i.d. property holds even if the original data display highly persistent dependence. Hong, Tu and Zhou (2007, Review of Financial Studies) develop copula-based tests for asymmetric dependence in asset returns and assess their economic implications. + +Professor Hong has recently started a research on modeling interval-valued time series data. An interval-valued observation in a time period contains more information than a point-valued observation in the same time period. Examples of interval data include the maximum and minimum temperatures in a day, the maximum and minimum GDP growth rates in a year, the maximum and minimum stock prices in a trading day, the ask and bid prices in a trading period, the long term and short term interest, and the 90%-tile and 10%-tile incomes of a cohort in a year, etc. Interval forecasts may be of direct interest in practice, as it contains information on the range of variation and the level of economic variables. Moreover, the informational advantage of interval data can be exploited for more efficient econometric estimation and inference. Hong and his coauthors propose a new class of autoregressive conditional interval (ACI) models for interval-valued time series data. A minimum distance estimation method is developed to estimate the parameters of an ACI model. Both simulation and empirical studies show that the use of interval time series data can provide more accurate estimation for model parameters in terms of mean squared error criterion. + +Another current research of Professor Hong is econometrics of time-varying models, where parameters in time series models are changing over time, due to (e.g.) technology progress, population change, preference shift, policy shock, and institutional reforms. Chen and Hong (2012, Econometrica) propose a nonparametric test for smooth structural changes in time series regression models, and Chen and Hong (2016, Econometric Theory) propose a nonparametric test for smooth structural changes in time series volatility models. Hong, Wang and Wang (2017, International Economic Review) propose a model-free test for strict stationarity for time series data against alternatives of evolutionary structural changes, and Sun, Hong and Wang (2018, Working paper) proposes a nonparametric method to distinguish whether a structural change in a time series data, if any, is abrupt structural break or smooth structural change. In a rolling out-of-sample forecast framework, with smooth-changing parameters, Hong, Sun and Wang (2018, Working paper) propose a data-driven method to determine the optimal rolling window that will yield the best out of sample forecasts in terms of the forecast mean squared error criterion. + +Professor Hong has also worked on evaluation of the Chinese economic policies. Examples include empirical evaluation of the effectiveness of the Chinese government policy to support for new strategic industries, and the impact of construction of high-speed railways on local economy in China (see, e.g., Ke, Chen, Hong and Hsiao (2017, China Economic Review)). + +# Biography[中文] + +# Interests[English] + +# Interests[中文] + +# Education[English] + +# Education[中文] + +# Awards[English] + +# Awards[中文] + +# Talks[English] + +# Talks[中文] + +# Work experience[English] +- 洪永淼，厦门大学经济学院院长，王亚南经济研究院的创院院长。 +# Work experience[中文] + +# Publication[English] +- **A Model-free Approach to Testing Instability of a Time Series Regression Model**, **Hong, Y. M.** , with Z. Fu, Forthcoming in Journal of Econometrics. +- **Threshold Autoregressive Models for Interval-valued Time Series Data**, **Hong, Y. M.** , with Y. Sun, A. Han, and S. Wang, Journal of Econometrics 206 (2018), 414--446. +- **Characteristic Function-based Testing for Conditional Independence via a Nonparametric Regression Approach**, **Hong, Y. M.** , with X. Wang, Econometric Theory 34 (2018), 815-849, doi:10.1017/S026646661700010X. +- **Testing Strict Stationarity , with Applications to Macroeconomic Time Series**, **Hong, Y. M.** , with X. Wang and S. Wang, International Economic Review 58 (2017), 1227-1277. +- **Detecting for Smooth Structural Changes in GARCH Models**, **Hong, Y. M.** , with Bin Chen, Econometric Theory 32 (2016), 740-791. +- **A Unified Approach to Validating Univariate and Multivariate Conditional Distribution Models in Time Series**, **Hong, Y. M.** , with B. Chen, Journal of Econometrics 178 (2014), 22-44. +- **A Loss Function Approach to Model Specification Testing and Its Relative Efficiency**, **Hong, Y. M.** , with Y. Lee, Annals of Statistics 41 (2013), 1166-1203. +- **Testing for Smooth Structural Changes in Time Series Models via Nonparametric Regression**, **Hong, Y. M.** , with B. Chen, Econometrica 80 (2012), 1157-1183. +- **Testing for the Markov Property in Time Series**, **Hong, Y. M.** , with B. Chen, Econometric Theory 28 (2012), 130-178. +- **Testing the Structure of Conditional Correlations in Multivariate GARCH Models: A Generalized Cross-Spectrum Approach**, **Hong, Y. M.** , with N. McCloud, International Economic Review 52 (2011), 991-1037. +- **Generalized Spectral Testing for Multivariate Continuous-time Models**, **Hong, Y. M.** , with B. Chen, Journal of Econometrics 164 (2011), 268-293. +- **Detecting Misspecifications in Autoregressive Conditional Duration Models and Non-negative Time-series Processes**, **Hong, Y. M.** , with Y.-J. Lee, Journal of Time Series Analysis 32 (2011), 1-32. +- **Characteristic Function-based Testing for Multifactor Continuous-time Markov Models via Nonparametric Regression**, **Hong, Y. M.** , with B. Chen, Econometric Theory 26 (2010), 1115-1179. +- **Granger causality in risk and detection of extreme risk spillover between financial markets**, **Hong, Y. M.** , with Y. Liu and S. Wang, Journal of Econometrics 150 (2009), 271-287. +- **Model-Free Evaluation of Directional Predictability in Foreign Exchange Markets**, **Hong, Y. M.** , with J. Chung, Journal of Applied Econometrics 22 (2007), 855-889. +- **Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation**, **Hong, Y. M.** , with J. Tu and G. Zhuo, Review of Financial Studies 20 (2007), 1547-1581. +- **Can the Random Walk Model be Beaten in Out-of-Sample Density Forecasts: Evidence from Intraday Foreign Exchange Rates**, **Hong, Y. M.** , with H. Li and F. Zhao, Journal of Econometrics 141 (2007), 736-776. +- **An improved generalized spectral test for time series models, with conditional heteroskedasticity of unknown form**, **Hong, Y. M.** , with Y. Lee, Econometric Theory 23, 106-154. +- **Validating Forecasts of the Joint Probability Density of Bond Yields: Can Affine Models Beat Random Walk?**, **Hong, Y. M.** , with A. Egorov and H. Li, Journal of Econometrics 135 (2006), 255-284. +- **Asymptotic theory for nonparametric entropy-based measure of serial dependence**, **Hong, Y. M.** , with H. White, Econometrica 73 (2005), 837-901. +- **Generalized spectral testing for conditional mean models in time series, with conditional heteroskedasticity of unknown form**, **Hong, Y. M.** , with Y. Lee, Review of Economic Studies 72 (2005), 499-541. +- **Nonparametric specification testing for continuous-time models, with applications to spot interest rates**, **Hong, Y. M.** , with H. Li, Review of Financial Studies 18 (2005), 37-84. +- **Wavelet-based consistent testing for serial correlation in panel models**, **Hong, Y. M.** , with D.Kao, Econometrica 72 (2004), 1519-1563. +- **Out-of-sample performance of discrete-time short-term interest models**, **Hong, Y. M.** , with H. Li and F. Zhao, Journal of Business and Economic Statistics 22 (2004), 457-473. +- **Inference on predictability of exchange rates via generalized spectrum and nonlinear time series models**, **Hong, Y. M.** , with T. H. Lee, Review of Economics and Statistics 85 (2003), 1048-1062. +- **Diagnostic checking for the adequacy of nonlinear time series models**, **Hong, Y. M.** , with T.H. Lee, Econometric Theory 19 (2003), 1065-1121. +- **One-sided testing for ARCH effects using wavelets**, **Hong, Y. M.** , with J. Lee, Econometric Theory 17 (2001), 1051-1081. +- **A test for volatility spillover , with application to exchange rates**, **Hong, Y. M.** Journal of Econometrics 103 (2001), 183-224. +- **Testing serial correlation of unknown form via wavelet methods**, **Hong, Y. M.** , with J. Lee, Econometric Theory 17 (2001), 386-423. +- **Generalized spectral tests for serial dependence, Journal of the Royal Statistical Society**, **Hong, Y. M.**，Series B (Statistical Methodology), 62 (2000), 557.574. +- **Hypothesis testing in time series via the empirical characteristic function: a generalized spectral density approach**, **Hong, Y. M.**，Journal of the American Statistical Association 94 (1999), 1201-1220. +- **A new ARCH test and its finite sample performance**, **Hong, Y. M.** , with R. Shehadeh, Journal of Business and Economic Statistics 17 (1999), 91-108. +- **Testing for pairwise serial independence via the empirical distribution function**, **Hong, Y. M.**，Journal of the Royal Statistical Society, Series B (Statistical Methodology), 60 (1998), 429-453. +- **One-sided testing for autoregressive conditional heteroskedasticity in time series models**, **Hong, Y. M.**，Journal of Time Series Analysis 18 (1997), 253-277. +- **Testing for independence between two covariance stationary time series**, **Hong, Y. M.**，Biometrika 83 (1996), 615-625. +- **Consistent testing for serial correlation of unknown form**, **Hong, Y. M.**，Econometrica 64 (1996), 837-864. +- **Consistent specification testing via nonparametric series regressions**, **Hong, Y. M.** , with H. White, Econometrica 63 (1995), 1133-1159. +- **China's evolving managerial labor market**, **Hong, Y. M.** , with T. Groves, J. McMillan and B. Naughton, Journal of Political Economy 103 (1995), 873-892. +- **Autonomy and incentives in Chinese state enterprises**, **Hong, Y. M.** , with T. Groves, J. McMillan and B. Naughton, Quarterly Journal of Economics CIX (1994), 183-209. + +# Publication[中文] + +# Information Reference + +# Notes \ No newline at end of file diff --git a/Data/CU-DSS/CU-DSS.csv b/Data/CU-DSS/CU-DSS.csv new file mode 100644 index 0000000..7bef120 --- /dev/null +++ b/Data/CU-DSS/CU-DSS.csv @@ -0,0 +1,29 @@ +name,profile,title,presonemail,homepage +John Abowd ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/JohnAbowd_crop.jpg?itok=U20Ek82Z,EDMUND EZRA DAY PROFESSOR OF ECONOMICS,John.abowd@cornell.edu,https://blogs.cornell.edu/abowd/ +Sumanta Basu ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/basu%20sumanta.jpg?itok=A8fvwHje,ASSISTANT PROFESSOR,sb2457@cornell.edu,http://faculty.bscb.cornell.edu/~basu/ +James Booth ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Jim%20Booth_crop.jpg?itok=3hqwdDQ-,PROFESSOR; DIRECTOR OF GRADUATE STUDIES,jim.booth@cornell.edu, +Florentina Bunea ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Florentina%20BuneaCrop.jpg?itok=9U1cMeZb,PROFESSOR,fb238@cornell.edu,https://bunea.stat.cornell.edu/ +Thomas DiCiccio ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/diciccio.jpg?itok=ZAWAzVe0,ASSOCIATE PROFESSOR; DIRECTOR OF UNDERGRADUATE STUDIES,tjd9@cornell.edu, +Ahmed El Alaoui ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/El%20Alaoui%20Ahmed_0.jpg?itok=hopYkTeu,ASSISTANT PROFESSOR,ae333@cornell.edu,https://elalaoui.stat.cornell.edu/ +Jeremy Entner ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/entner%20jeremy%20edit.jpg?itok=42Y57gMz,LECTURER,jfe35@cornell.edu, +Dominique Fourdrinier ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/fourdrinier.jpg?itok=3t71bemW,ADJUNCT PROFESSOR,df274@cornell.edu, +Christophe Giraud ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/christophe2.png?itok=bh4Q4La-,ADJUNCT PROFESSOR, cg582@cornell.edu, +Joe Guinness ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/guiness.jpg?itok=sZ3aTlVb,ASSOCIATE PROFESSOR,guinness@cornell.edu,http://guinness.cals.cornell.edu/ +Yongmiao Hong ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/yhong.jpg?itok=Ng4C98lQ,PROFESSOR,yh20@cornell.edu, +Giles Hooker ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Giles%20Hooker_crop.jpg?itok=_ns9SEBF,PROFESSOR,gjh27@cornell.edu,http://faculty.bscb.cornell.edu/~hooker/ +Elizabeth Karns ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/karns.jpg?itok=TP88O20C,SENIOR LECTURER,karns@cornell.edu,https://www.ilr.cornell.edu/people/m-karns +Kara Karpman ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Karpman%20Kara.jpg?itok=cySsJEz-,ADJUNCT ASSISTANT PROFESSOR,kjk233@cornell.edu, +Kengo Kato ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/kato%20kengo.jpg?itok=JDh9_Hq9,PROFESSOR,kk976@cornell.edu,https://sites.google.com/site/kkatostat/home +Nicholas Kiefer ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Nicholas%20Kiefer_crop.jpg?itok=qVIs-_3g,Ta-Chung Liu Professor ,nicholas.kiefer@cornell.edu, +Amy Kuceyeski ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Amy_Kuceyeski.jpeg?itok=cJckdeGx,ADJUNCT ASSOCIATE PROFESSOR,amk2012@med.cornell.edu,http://vivo.med.cornell.edu/display/cwid-amk2012 +David S. Matteson ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/dave_matteson_crop.jpg?itok=-6ZYQz8g,ASSOCIATE PROFESSOR,matteson@cornell.edu,http://www.stat.cornell.edu/~matteson/ +Francesca Molinari ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/francesca-molinari_crop.jpg?itok=7rxbabYa,PROFESSOR,fm72@cornell.edu, +Yang Ning ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Yang-Ning-edit.jpg?itok=PuAOkZ3B,ASSISTANT PROFESSOR,yn265@cornell.edu,http://yangning.stat.cornell.edu/ +Michael Nussbaum ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/michael-nussbaum_crop.jpg?itok=DWnvgI00,PROFESSOR,nussbaum@math.cornell.edu,https://math.cornell.edu/michael-nussbaum +Kevin Packard ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Cornell%20Logo_0.jpg?itok=iv5-sYVv,VISITING LECTURER,kcp48@cornell.edu, +David Ruppert ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/Dave%20Ruppert_crop.jpg?itok=o86HBie6,ANDREW SCHULTZ JR. PROFESSOR OF ENGINEERING,dr24@cornell.edu,http://people.orie.cornell.edu/davidr/ +Melissa Smith ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/MelissaSmithCrop.jpg?itok=zHVVOUc7,SENIOR LECTURER,ms429@cornell.edu, +Y. Samuel Wang ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/wang%20y%20samuel.jpg?itok=TSUrORO6,ASSISTANT PROFESSOR,ysw7@cornell.edu,https://ysamuelwang.com/ +Marten Wegkamp ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/marten-wegkamp_crop.jpg?itok=eCzIOJVj,Professor , ,http://www.math.cornell.edu/~marten +Martin Wells ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/marty-wells_crop.jpg?itok=rjWP9XlK,DEPARTMENT CHAIR; CHARLES A. ALEXANDER PROFESSOR OF STATISTICAL SCIENCES,mtw1@cornell.edu, +Xiaolong Yang ,https://stat.cornell.edu/sites/default/files/styles/square_portrait/public/xiaolong-yang_crop.jpg?itok=ZF_W6atF,SENIOR LECTURER AND SENIOR ASSOCIATE DIRECTOR OF THE MPS PROGRAM,xy44@cornell.edu, diff --git a/tools/teacher_list.R b/tools/teacher_list.R index 6a37105..c0e616f 100644 --- a/tools/teacher_list.R +++ b/tools/teacher_list.R @@ -4,7 +4,7 @@ library(stringr) args <- commandArgs(T) folder <- args[1] -setwd("C:/Users/RY/git/stateacher") # 修改为自己的目录 +setwd("D:\应用程序\Git\stateacher") # 修改为自己的目录 path = paste0('Data/', folder) dat_tmp = read.csv(paste0(path, '/', folder, '.csv'), stringsAsFactors = FALSE)