Add data on keynote

Author: TuomasBorman

data/keynote_speakers.csv

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+1,Helena Kilpinen,,,"Helena Kilpinen is a tenure-track associate professor in human genetics and genomic medicine at the University of Helsinki, in the Helsinki Institute of Life Science (HiLIFE) and the Faculty of Medicine, and group leader at the Institute for Molecular Medicine Finland (FIMM) and the HiLIFE Neuroscience Center. She is interested in cellular genetics and the cellular basis of developmental and other brain-related disorders. In her research group, they use human induced pluripotent stem cells (iPSC) as models, and combine computational and experimental methods to study how genetic variation causes variability in cell phenotypes and contributes to differential susceptibility to diseases, both common and rare.",,Joint profiling of cell morphology and gene expression in neuronal disease models
+2,Levi Waldron,,,"Levi Waldron is Professor of Epidemiology and Biostatistics at the City University of New York, where he leads a research program in cancer genomics and in metagenomic profiling of the human microbiome, develops methods within the intersection of statistical analysis and computation, and works to develop an inclusive community of researchers and students around open-source bioinformatics software and methods. His research group aims to generate new insights into human health, disease, and treatment through improved tools and novel analysis of publicly available data. He is an active contributor to the Bioconductor project and member of its Technical Advisory Board.",,Curated metagenomic databases for meta-analysis of the microbiome and disease
+3,Anders Krogh,,,"Anders Krogh is professor in the Department of Computer Science and the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences at the University of Copenhagen. He has worked in many areas of bioinformatics and machine learning, both with theory and applications, particularly on hidden Markov models for biological sequences. In recent years, he has focused on deep generative models and applied them to gene expression data and other bio/medical data.",,A deep generative model applied to gene expression data and multimodal data
+4,Aura Raulo,,,"Probabilistic models for complex spreading processes in contact networks – examples from microbiome transmission  Many things spread in social contact networks – microbes, information, memes – and many methods exist for tracking the spread of these agents across networks. However, spreading agents rarely spread alone – social contact can function as a way to spread whole communities of microbes, or whole sets of ideas. Much fewer tools exist to describe complex spreading processes of sets of things spreading in parallel in the same networks.  A good example of this kind of spreading set is the gut microbiome, our internal ecosystem of hundreds of healthy microbial species that, just like disease, can spread from individual to another in social contact. Social transmission of healthy gut microbiomes offers a great system to develop new models of complex spread  Here I will introduce a Probabilistic modeling framework for studying complex spreading processes, based on data on contact networks and distribution of shared elements in a population of interacting individuals. This model predicts frequency of shared elements with frequency of contact among pairs of individuals, using a multimembership random structure to account for dependance among pairwise values. I will show how such a model can be used to reveal the extent of transmission of gut microbiomes in animal social networks and what kind of follow-up research directions it offers, such as: What kind of microbes spread through which kind of contact or what kind of positions in a social network accumulate most diverse set of microbes. ",,