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update resume + add some recent talks
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index.md

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pip install fastgps
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```
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Gaussian process regression (GPR) models typically require $\mathcal{O}(n^2)$ storage and $\mathcal{O}(n^3)$ computations. [FastGPs](https://alegresor.github.io/fastgps) implements GPR which requires only $\mathcal{O}(n)$ storage and $\mathcal{O}(n \log n)$ computations by pairing certain quasi-random sampling locations with matching kernels to yield structured Gram matrices. We support
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Gaussian process (GP) regression models typically require $\mathcal{O}(n^2)$ storage and $\mathcal{O}(n^3)$ computations. [FastGPs](https://alegresor.github.io/fastgps) implements GPs which requires only $\mathcal{O}(n)$ storage and $\mathcal{O}(n \log n)$ computations by pairing certain quasi-random sampling locations with matching kernels to yield structured Gram matrices. We support
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- GPU scaling,
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- batched inference,
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- robust hyperparameter optimization, and
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- multi-task GPR.
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- multitask GPs.
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![image](./assets/2d_gp.svg)
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## A Neural Surrogate Solver for Radiation Transfer
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[2024 NeurIPS Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers](https://neurips.cc/virtual/2024/workshop/84720)
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[2024 NeurIPS Workshop on Data-Driven and Differentiable Simulations, Surrogates, and Solvers](https://neurips.cc/virtual/2024/workshop/84720)
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<embed src="./posters/2024_RTEDeepONet_NeurIPSD3S3.pdf" type="application/pdf" width="1000" height="1500"/>
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- [QMCPy: A Quasi-Monte Carlo Software in Python 3.](./posters/2021_QMCPy_SIAMCSE.pdf) @ [2021 SIAM Conference on Computational Science and Engineering](https://www.siam.org/conferences/cm/conference/cse21)
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- [Multi-threaded/-processed Requests to Cloud Services for Intelligent Address Standardization](./posters/2019_PRLAS_SIAMCSE.pdf) @ [2019 SIAM Conference on Computational Science and Engineering](https://www.siam.org/conferences/cm/conference/cse19)
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- [Multithreaded/multiprocessed Requests to Cloud Services for Intelligent Address Standardization](./posters/2019_PRLAS_SIAMCSE.pdf) @ [2019 SIAM Conference on Computational Science and Engineering](https://www.siam.org/conferences/cm/conference/cse19)
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# Presentations
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## Other Presentations
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- [Quasi-Monte Carlo and Fast Multi-Task Gaussian Process Regression](./presentations/2025_QMCFastMTGPs_Caltech.pdf) @ 2025 Caltech Lunch Group Seminar
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- [Scientific Machine Learning for Exact Recovery of Nonlinear PDEs](./presentations/2025_CHONKNORIS_CompMathIIT.pdf) 2024 Illinois Institute of Technology, Department of Applied Mathematics, Computational Mathematics Seminar
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- [Software for Quasi-Monte Carlo and Fast Gaussian Process Regression](./presentations/2025_FastMathUQ_Sandia.pdf) @ 2025 FastMathUQ Seminar at Sandia National Laboratories.
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- [Quasi-Monte Carlo and Fast Multitask Gaussian Process Regression](./presentations/2025_QMCFastMTGPs_Caltech.pdf) @ 2025 Caltech Lunch Group Seminar
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- [Fast Gaussian Process Regression with Derivative Information using Lattice and Digital Sequences](./presentations/2024_PhDComp_IIT.pdf) @ 2024 Illinois Institute of Technology PhD Comprehensive Exam
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- [Fast Gaussian Process Regression for Smooth Functions using Lattice and Digital Sequences with Matching Kernels](./presentations/2024_HODNKernels_MCQMC.pdf) @ [2024 Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing Conference](https://uwaterloo.ca/monte-carlo-methods-scientific-computing-conference/)
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- [Walsh Functions and Spaces](./presentations/2024_WalshFunctions_IIT.pdf) @ 2024 Illinois Institute of Technology, Department of Applied Mathematics, Computational Mathematics and Multiscale Seminar
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- [(Quasi)-Monte Carlo Importance Sampling with QMCPy.](./presentations/2021_QMCPyIS_CompMathIIT.pdf) @ 2021 Illinois Institute of Technology, Department of Applied Mathematics, Computational Mathematics Seminar
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- [QMCPy: A Quasi-Monte Carlo Software in Python 3](./presentations/2020_QMCPy_CASSC.pdf) @ [2020 Chicago Area SIAM Student Conference](https://siam-northwestern.github.io/cassc_2020.html)
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- [QMCPy: A Quasi-Monte Carlo Software in Python 3.](./presentations/2020_QMCPy_PyDataChicago.pdf) @ [2020 PyData Chicago](https://chicago.pydata.org/)
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# Photo Gallery
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<img src="assets/photos/IMG_4241.jpeg"/>
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resume/ags.bib

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% rebiber -i main.bib
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% https://flamingtempura.github.io/bibtex-tidy/index.html
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% bibtex-tidy --omit=abstract --curly --months --align=20 --sort=year,key --duplicates=key,doi,citation,abstract --merge=overwrite --sort-fields --trailing-commas --encode-urls --no-tidy-comments --remove-empty-fields main.bib
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@article{sorokin.QMC_IS_QMCPy,
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title = {({Q}uasi-){M}onte {C}arlo Importance Sampling with {QMCPy}},
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author = {Sorokin, Aleksei G. and Hickernell, Fred J. and Choi, Sou-Cheng T. and McCourt, Michael J. and Rathinavel, Jagadeeswaran},
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year = {2021},
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journal = {IIT Undergraduate Research Journal},
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pages = {49--54},
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url = {http://urj.library.iit.edu/index.php/urj/article/view/48},
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}
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@inproceedings{choi.challenges_great_qmc_software,
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title = {Challenges in developing great quasi-{M}onte {C}arlo software},
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author = {Choi, Sou-Cheng T. and Ding, Yuhan and Hickernell, Fred J. and Rathinavel, Jagadeeswaran and Sorokin, Aleksei G.},
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year = {2022},
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booktitle = {{M}onte {C}arlo and Quasi-{M}onte {C}arlo Methods 2022},
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pages = {209--222},
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editor = {Hinrichs, Aicke and Kritzer, Peter and Pillichshammer, Friedrich},
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organization = {Springer},
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}
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@inproceedings{choi.QMC_software,
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title = {Quasi-{M}onte {C}arlo software},
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author = {Choi, Sou-Cheng T. and Hickernell, Fred J. and Rathinavel, Jagadeeswaran and McCourt, Michael J. and Sorokin, Aleksei G.},
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year = {2022},
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booktitle = {{M}onte {C}arlo and Quasi-{M}onte {C}arlo Methods 2020},
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publisher = {Springer International Publishing},
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address = {Cham},
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pages = {23--47},
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isbn = {978-3-030-98319-2},
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editor = {Keller, Alexander},
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}
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@inproceedings{sorokin.MC_vector_functions_integrals,
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title = {On bounding and approximating functions of multiple expectations using quasi-{M}onte {C}arlo},
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author = {Sorokin, Aleksei G. and Rathinavel, Jagadeeswaran},
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year = {2022},
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booktitle = {{M}onte {C}arlo and Quasi-{M}onte {C}arlo Methods 2022},
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pages = {583--599},
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editor = {Hinrichs, Aicke and Kritzer, Peter and Pillichshammer, Friedrich},
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organization = {Springer},
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}
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@misc{FastGaussianProcesses.jl,
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title = {Fast{G}aussian{P}rocesses.jl},
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author = {Aleksei G. Sorokin},
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year = {2023},
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url = {https://github.com/alegresor/FastGaussianProcesses.jl},
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}
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@article{GalCEM.software,
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title = {Gal{CEM}: galactic chemical evolution model},
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author = {Gjergo, Eda and Sorokin, Aleksei G. and Ruth, Anthony and Spitoni, Emanuele and Matteucci, Francesca and Fan, Xilong and Liang, Jinning and Limongi, Marco and Yamazaki, Yuta and Kusakabe, Motohiko and others},
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year = {2023},
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journal = {Astrophysics Source Code Library},
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pages = {ascl--2301},
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}
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@misc{gjergo.GalCEM1,
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title = {Gal{CEM}. {I}. {A}n Open-source Detailed Isotopic Chemical Evolution Code},
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author = {Eda Gjergo and Aleksei G. Sorokin and Anthony Ruth and Emanuele Spitoni and Francesca Matteucci and Xilong Fan and Jinning Liang and Marco Limongi and Yuta Yamazaki and Motohiko Kusakabe and Toshitaka Kajino},
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year = {2023},
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journal = {The Astrophysical Journal Supplement Series},
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publisher = {The American Astronomical Society},
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volume = {264},
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number = {2},
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pages = {44},
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doi = {10.3847/1538-4365/aca7c7},
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url = {https://dx.doi.org/10.3847/1538-4365/aca7c7},
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}
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@misc{QMCGenerators.jl,
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title = {{QMCG}enerators.jl},
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author = {Aleksei G. Sorokin},
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year = {2023},
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url = {https://github.com/alegresor/QMCGenerators.jl},
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}
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@misc{QuasiGaussianProcesses.jl,
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title = {Quasi{G}aussian{P}rocesses.jl},
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author = {Aleksei G. Sorokin},
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year = {2023},
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url = {https://github.com/alegresor/QuasiGaussianProcesses.jl},
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}
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@article{sorokin.adaptive_prob_failure_GP,
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title = {Credible intervals for probability of failure with {G}aussian processes},
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author = {Aleksei G. Sorokin and Vishwas Rao},
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year = {2023},
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journal = {ArXiv preprint},
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volume = {abs/2311.07733},
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url = {https://arxiv.org/abs/2311.07733},
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}
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@article{sorokin.sigopt_mulch,
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title = {Sig{O}pt {M}ulch: an intelligent system for {A}uto{ML} of gradient boosted trees},
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author = {Aleksei G. Sorokin and Xinran Zhu and Eric Hans Lee and Bolong Cheng},
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year = {2023},
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journal = {Knowledge-Based Systems},
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pages = {110604},
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doi = {10.1016/j.knosys.2023.110604},
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issn = {0950-7051},
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url = {https://www.sciencedirect.com/science/article/pii/S0950705123003544},
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}
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@article{sorokin.gp4darcy,
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title = {Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media},
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author = {Aleksei G. Sorokin and Aleksandra Pachalieva and Daniel O'Malley and James M. Hyman and Fred J. Hickernell and Nicolas W. Hengartner},
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year = {2024},
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journal = {Advances in Water Resources},
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volume = {193},
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pages = {104836},
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doi = {10.1016/j.advwatres.2024.104836},
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issn = {0309-1708},
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url = {https://www.sciencedirect.com/science/article/pii/S0309170824002239},
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}
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@inproceedings{sorokin.RTE_DeepONet,
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title = {A neural surrogate solver for radiation transfer},
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author = {Aleksei G. Sorokin and Xiaoyi Lu and Yi Wang},
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year = {2024},
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booktitle = {NeurIPS 2024 Workshop on Data-Driven and Differentiable Simulations, Surrogates, and Solvers},
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url = {https://openreview.net/forum?id=SHidR8UMKo},
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}
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@book{bacho.CHONKNORIS.tmp,
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title = {Operator learning at machine precision},
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author = {Aras Bacho and Aleksei G. Sorokin and Xianjin Yang and Th\'{e}o Bourdais and Edoardo Calvello and Matthieu Darcy and Alexander Hsu and Bamdad Hosseini and Houman Owhadi},
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year = {2025},
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note = {In preparation},
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}
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@article{gjergo2025massive,
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title = {Massive star formation at supersolar metallicities: constraints on the initial mass function},
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author = {Gjergo, Eda and Zhang, Zhiyu and Kroupa, Pavel and Sorokin, Aleksei G. and Yan, Zhiqiang and Guo, Ziyi and Jerabkova, Tereza and Zoonozi, Akram Hasani and Haghi, Hosein},
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year = {2025},
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journal = {ArXiv preprint},
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volume = {abs/2509.20440},
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url = {https://arxiv.org/abs/2509.20440},
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}
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@article{hickernell.qmc_what_why_how,
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title = {Quasi-{M}onte {C}arlo methods: what, why, and how?},
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author = {Fred J. Hickernell and Nathan Kirk and Aleksei G. Sorokin},
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year = {2025},
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journal = {ArXiv preprint},
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volume = {abs/2502.03644},
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url = {https://arxiv.org/abs/2502.03644},
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}
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@article{jain.bernstein_betting_confidence_intervals,
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title = {Empirical {B}ernstein and betting confidence intervals for randomized quasi-{M}onte {C}arlo},
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author = {Aadit Jain and Fred J. Hickernell and Art B. Owen and Aleksei G. Sorokin},
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year = {2025},
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journal = {ArXiv preprint},
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volume = {abs/2504.18677},
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url = {https://arxiv.org/abs/2504.18677},
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}
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@article{sorokin.2025.ld_randomizations_ho_nets_fast_kernel_mats,
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title = {{QMCPy}: a {P}ython software for randomized low-discrepancy sequences, quasi-{M}onte {C}arlo, and fast kernel methods},
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author = {Aleksei G. Sorokin},
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year = {2025},
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journal = {ArXiv preprint},
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volume = {abs/2502.14256},
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url = {https://arxiv.org/abs/2502.14256},
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}
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@article{sorokin.FastBayesianMLQMC,
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title = {Fast {B}ayesian multilevel quasi-{M}onte {C}arlo},
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author = {Aleksei G. Sorokin and Pieterjan Robbe and Gianluca Geraci and Michael S. Eldred and Fred J. Hickernell},
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year = {2025},
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journal = {ArXiv preprint},
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volume = {abs/2510.24604},
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url = {https://arxiv.org/abs/2510.24604},
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}
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@inproceedings{sorokin.fastgps_probnum25,
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title = {Fast {G}aussian process regression for high dimensional functions with derivative information},
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author = {Sorokin, Aleksei G. and Robbe, Pieterjan and Hickernell, Fred J.},
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year = {2025},
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booktitle = {Proceedings of the First International Conference on Probabilistic Numerics},
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publisher = {{PMLR}},
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series = {Proceedings of Machine Learning Research},
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volume = {271},
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pages = {35--49},
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url = {https://proceedings.mlr.press/v271/sorokin25a.html},
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editor = {Kanagawa, Motonobu and Cockayne, Jon and Gessner, Alexandra and Hennig, Philipp},
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pdf = {https://raw.githubusercontent.com/mlresearch/v271/main/assets/sorokin25a/sorokin25a.pdf},
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}

resume/sorokin_resume.pdf

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