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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)

Neda Masoud

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The future of transportation lies at the intersection of two emerging trends, namely, the sharing economy and connected and automated vehicle technology. Our research group investigates the impact of these two major trends on the future of mobility, quantifying the benefits and identifying the challenges of integrating these technologies into our current systems.

Our research on shared-use mobility systems focuses on peer-to-peer (P2P) ridesharing and multi-modal transportation. We provide: (i) operational tools and decision support systems for shared-use mobility in legacy as well as connected and automated transportation systems. This line of research focuses on system design as well as routing, scheduling, and pricing mechanisms to serve on-demand transportation requests; (ii) insights for regulators and policy makers on mobility benefits of multi-modal transportation; (ii) planning tools that would allow for informed regulations of sharing economy.

In another line of research we investigate challenges faced by the connected automated vehicle technology before mass adoption of this technology can occur. Our research mainly focuses on (i) transition of control authority between the human driver and the autonomous entity in semi-autonomous (level 3 SAE autonomy) vehicles; (ii) incorporating network-level information supplied by connected vehicle technology into traditional trajectory planning; (iii) improving vehicle localization by taking advantage of opportunities provided by connected vehicles; and (iv) cybersecurity challenges in connected and automated systems. We seek to quantify the mobility and safety implications of this disruptive technology, and provide insights that can allow for informed regulations.

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.

Patrick Schloss

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The Schloss lab is broadly interested in beneficial and pathogenic host-microbiome interactions with the goal of improving our understanding of how the microbiome can be used to reach translational outcomes in the prevention, detection, and treatment of colorectal cancer, Crohn’s disease, and Clostridium difficile infection. To address these questions, we test traditional ecological theory in the microbial context using a systems biology approach. Specifically, the laboratory specializes in using studies involving human subjects and animal models to understand how biological diversity affects community function using a variety of culture-independent genomics techniques including sequencing 16S rRNA gene fragments, metagenomics, and metatranscriptomics. In addition, they use metabolomics to understand the functional role of the gut microbiota in states of health and disease. To support these efforts, they develop and apply bioinformatic tools to facilitate their analysis. Most notable is the development of the mothur software package (https://www.mothur.org), which is one of the most widely used tools for analyzing microbiome data and has been cited more than 7,300 times since it was initially published in 2009. The Schloss lab deftly merges the ability to collect data to answer important biological questions using cutting edge wet-lab techniques and computational tools to synthesize these data to answer their biological questions.

Given the explosion in microbiome research over the past 15 years, the Schloss lab has also stood at the center of a major effort to train interdisciplinary scientists in applying computational tools to study complex biological systems. These efforts have centered around developing reproducible research skills and applying modern data visualization techniques. An outgrowth of these efforts at the University of Michigan has been the institutionalization of The Carpentries organization on campus (https://carpentries.org), which specializes in peer-to-peer instruction of programming tools and techniques to foster better reproducibility and build a community of practitioners.

The Schloss lab uses computational tools to integrate multi-omics tools in a culture-independent approach to understand how bacteria interact with each other and their host to drive processes such as colorectal cancer and susceptibility to Clostridium difficile infections.

Mousumi Banerjee

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My research is primarily focused around 1) machine learning methods for understanding healthcare delivery and outcomes in the population, 2) analyses of correlated data (e.g. longitudinal and clustered data), and 3) survival analysis and competing risks analyses. We have developed tree-based and ensemble regression methods for censored and multilevel data, combination classifiers using different types of learning methods, and methodology to identify representative trees from an ensemble. These methods have been applied to important areas of biomedicine, specifically in patient prognostication, in developing clinical decision-making tools, and in identifying complex interactions between patient, provider, and health systems for understanding variations in healthcare utilization and delivery. My substantive areas of research are cancer and pediatric cardiovascular disease.

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.

 

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.

Kai S. Cortina

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Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.