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Yongsheng Bai

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Dr. Bai’s research interests lie in development and refinement of bioinformatics algorithms/software and databases on next-generation sequencing (NGS data), development of statistical model for solving biological problems, bioinformatics analysis of clinical data, as well as other topics including, but not limited to, uncovering disease genes and variants using informatics approaches, computational analysis of cis-regulation and comparative motif finding, large-scale genome annotation, comparative “omics”, and evolutionary genomics.

Veera Baladandayuthapani

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Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.

 

An example of horizontal (across cancers) and vertical (across multiple molecular platforms) data integration. Image from Ha et al (Nature Scientific Reports, 2018; https://www.nature.com/articles/s41598-018-32682-x)

Xun Huan

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Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.

Optimal experimental design seeks to identify experiments that produce the most valuable data. For example, when designing a combustion experiment to learn chemical kinetic parameters, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we indeed obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.

Xiang Zhou

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My research is focused on developing efficient and effective statistical and computational methods for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies, epigenome-wide association studies, and various functional genomic sequencing studies such as bulk and single cell RNAseq, bisulfite sequencing, ChIPseq, ATACseq etc. Our method development is often application oriented and specifically targeted for practical applications of these large-scale genetic and genomic studies, thus is not restricted in a particular methodology area. Our previous and current methods include, but are not limited to, Bayesian methods, mixed effects models, factor analysis models, sparse regression models, deep learning algorithms, clustering algorithms, integrative methods, spatial statistics, and efficient computational algorithms. By developing novel analytic methods, I seek to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease related quantitative traits.

A statistical method recently developed in our group aims to identify tissues that are relevant to diseases or disease related complex traits, through integrating tissue specific omics studies (e.g. ROADMAP project) with genome-wide association studies (GWASs). Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis) evaluated by our method. Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.

Yuki Shiraito

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Yuki Shiraito works primarily in the field of political methodology. His research interests center on the development and applications of Bayesian statistical models and large-scale computational algorithms for data analysis. He has applied these quantitative methods to political science research including a survey experiment on public support for conflicting parties in civil war, heterogeneous effects of indiscriminate state violence, and the detection of text diffusion among a large set of legislative bills.

After completing his undergraduate education at the University of Tokyo, Yuki received his Ph.D. in Politics (2017) from Princeton University. Before joining the University of Michigan as an Assistant Professor in September 2018, he served as a Postdoctoral Fellow in the Program of Quantitative Social Science at Dartmouth College.

Raed Al Kontar

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My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

 

Ho-Joon Lee

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Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.

Figure 1. Proteomics and metabolomics analysis of IL-3 activation and cell cycle (Lee et al., Cell Reports 2017). (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.

 

Erhan Bayraktar

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Erhan Bayraktar, PhD, the holder of the Susan Smith Chair, is a full professor of Mathematics at the University of Michigan, where he has been since 2004. Professor Bayraktar’s research is in stochastic analysis, control, applied probability and mathematical finance. He has over 120 publications in top journals in these areas.

Professor Bayraktar is recognized as a leader in his areas of research: he is a corresponding editor in the SIAM Journal on Control and Optimization and also serves in the editorial boards of Applied Mathematics and Optimization, Mathematics of Operations Research, Mathematical Finance. His research has been also been continually funded by the National Science Foundation; in particular, he received a CAREER grant.

Professor Bayraktar has also been devoting his time to teaching and synergistic activities: Professor Bayraktar has been the director of the Risk Management and Quantitative Finance Masters program since its inception in 2015. As one of the two organizers of the Financial/Actuarial Math seminar which brings about 10-15 speakers every academic year and he has also organized several international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Areas of interest: Mathematical finance, applied probability, stochastic analysis, stochastic control, optimal stopping.

Samuel K Handelman

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Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

Romesh Saigal

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Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.