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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.

Jun Li

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Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.

 Prof. Li’s areas of interest include genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.

Matias D. Cattaneo

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Matias D. Cattaneo, Ph.D., is Professor of Economics and Statistics in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cattaneo’s research interests include econometric theory, mathematical statistics, and applied econometrics, with focus on causal inference, program evaluation, high-dimensional problems and applied microeconomics. Most of his recent research relates to the development of new, improved semiparametric, nonparametric and high-dimensional inference procedures exhibiting demonstrable superior robustness properties with respect to tuning parameter and other implementation choices. His work is motivated by concrete empirical problems in social, biomedical and statistical sciences, covering a wide array of topics in settings related to treatment effects and policy evaluation, high-dimensional models, average derivatives and structural response functions, applied finance and applied decision theory, among others.

Ding Zhao

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Ding Zhao, PhD, is Assistant Research Scientist in the department of Mechanical Engineering, College of Engineering with a secondary appointment in the Robotics Institute at The University of Michigan, Ann Arbor.

Dr. Zhao’s research interests include autonomous vehicles, intelligent/connected transportation, traffic safety, human-machine interaction, rare events analysis, dynamics and control, machine learning, and big data analysis

 

Sriram Chandrasekaran

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Sriram Chandrasekaran, PhD, is Assistant Professor of Biomedical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Dr. Chandrasekaran’s Systems Biology lab develops computer models of biological processes to understand them holistically. Sriram is interested in deciphering how thousands of proteins work together at the microscopic level to orchestrate complex processes like embryonic development or cognition, and how this complex network breaks down in diseases like cancer. Systems biology software and algorithms developed by his lab are highlighted below and are available at http://www.sriramlab.org/software/.

– INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)

– GEMINI (Gene Expression and Metabolism Integrated for Network Inference) is a network curation tool. It allows rapid assessment of regulatory interactions predicted by high-throughput approaches by integrating them with a metabolic network (Ref: Chandrasekaran and Price, PloS Computational Biology 2013)

– ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. (Ref: Chandrasekaran et al. PNAS 2011)

– PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models (Ref: Chandrasekaran and Price, PNAS 2010)

 

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Yuekai Sun

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Yuekai Sun, PhD, is Assistant Professor in the department of Statistics at the University of Michigan, Ann Arbor.

Dr. Sun’s research is motivated by the challenges of analyzing massive data sets in data-driven science and engineering. I focus on statistical methodology for high-dimensional problems; i.e. problems where the number of unknown parameters is comparable to or exceeds the sample size. My recent work focuses on two problems that arise in learning from high-dimensional data (versus black-box approaches that do not yield insights into the underlying data-generation process). They are:
1. model selection and post-selection inference: discover the latent low-dimensional structure in high-dimensional data and perform inference on the learned structure;
2. distributed statistical computing: design scalable estimators and algorithms that avoid communication and minimize “passes” over the data.
A recurring theme in my work is exploiting the geometry of latent low-dimensional structure for statistical and computational gains. More broadly, I am interested in the geometric aspects of high-dimensional data analysis.

A visualization of an algorithm for making accurate recommendations from data that contain shared user accounts.

A visualization of an algorithm for making accurate recommendations from data that contain shared user accounts.

 

Yi Li

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Yi Li is a Professor of Biostatistics and Director of the Kidney Epidemiology and Cost Center. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. He is developing methodologies for analyzing large-scale andhigh-dimensional datasets, with direct applications inobservational studies as well in genetics/genomics. His methodologic research is funded by various federal grants starting from year 2003. Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics.

Vijay Subramanian

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Professor Subramanian is interested in a variety of stochastic modeling, decision and control theoretic, and applied probability questions concerned with networks. Examples include analysis of random graphs, analysis of processes like cascades on random graphs, network economics, analysis of e-commerce systems, mean-field games, network games, telecommunication networks, load-balancing in large server farms, and information assimilation, aggregation and flow in networks especially with strategic users.

Emanuel Gull

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Professor Gull works in the general area of computational condensed matter physics with a focus on the study of correlated electronic systems in and out of equilibrium. He is an expert on Monte Carlo methods for quantum systems and one of the developers of the diagrammatic ‘continuous-time’ quantum Monte Carlo methods. His recent work includes the study of the Hubbard model using large cluster dynamical mean field methods, the development of vertex function methods for optical (Raman and optical conductivity) probes, and the development of bold line diagrammatic algorithms for quantum impurities out of equilibrium. Professor Gull is involved in the development of open source computer programs for strongly correlated systems.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system's equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system’s equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.