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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.


James Kibbie

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James Kibbie, DMA, is Professor and Chair of the Department of Organ in the School of Music, Theatre & Dance and University Organist at the University of Michigan, Ann Arbor.

Professor Kibbie’s current research will develop and analyze a library of digitized performances of Bach’s Trio Sonatas, applying novel algorithms to study the music structure from a data science perspective. The team’s analysis will compare different performances to determine features that make performances artistic, as well as the common mistakes performers make. Findings will be integrated into courses both on organ performance and on data science. The project Investigators are Daniel Forger, professor of mathematics and computational medicine and bioinformatics and Professor Kibbie.


S. Sriram

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S. Sriram, PhD, is Associate Professor of Marketing in the University of Michigan Ross School of Business, Ann Arbor.

Prof. Sriram’s research interests are in the areas of brand and product portfolio management, multi-sided platforms, healthcare policy, and online education. His research uses state of the art econometric methods to answer important managerial and policy-relevant questions. He has studied topics such as measuring and tracking brand equity and optimal allocation of resources to maintain long-term brand profitability, cannibalization, consumer adoption of technology products, and strategies for multi-sided platforms. Substantively, his research has spanned several industries including consumer packaged goods, technology products and services, retailing, news media, the interface of healthcare and marketing, and MOOCs.

Somangshu Mukherji

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Somangshu (Sam) Mukherji, PhD, is Assistant Professor of Music Theory in the School of Music, Theatre & Dance at the University of Michigan, Ann Arbor.

Sam Mukherji‘s work lies at the interface of traditional Western tonal theory, the theory and practice of popular and non-Western idioms, and the cognitive science of music. Within this framework, the main focus of his research has been on the prolongational, grammatical aspects of Western tonality, and their connection to the tonal structures of Indian music, and the blues-based traditions within rock and metal. This emphasis makes his work similar to that of a linguist who explores relationships between the world’s languages-and, therefore, Mukherji’s research has been influenced in particular by ideas from linguistic theory as well, especially the Minimalist Program in contemporary generative linguistics. For this reason, he has investigated connections not only between different musical idioms but also between music and language-and musical and linguistic theory-more generally. Much of his work explores overlaps between Minimalist linguistics, and related, generative approaches within music theory (such as those found in the writings of Heinrich Schenker), and he has also written extensively about what such ‘musicolinguistic’ connections imply for the wider study of human musical behavior, cognition, and evolution.

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.

Tim Cernak

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Tim Cernak, PhD, is Assistant Professor of Medicinal Chemistry with secondary appointments in Chemistry and the Chemical Biology Program at the University of Michigan, Ann Arbor.

The functional and biological properties of a small molecule are encoded within its structure so synthetic strategies that access diverse structures are paramount to the invention of novel functional molecules such as biological probes, materials or pharmaceuticals. The Cernak Lab studies the interface of chemical synthesis and computer science to understand the relationship of structure, properties and reactions. We aim to use algorithms, robotics and big data to invent new chemical reactions, synthetic routes to natural products, and small molecule probes to answer questions in basic biology. Researchers in the group learn high-throughput chemical and biochemical experimentation, basic coding, and modern synthetic techniques. By studying the relationship of chemical synthesis to functional properties, we pursue the opportunity to positively impact human health.

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.

Antonios M. Koumpias

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Antonios M. Koumpias, Ph.D., is Assistant Professor of Economics in the department of Social Sciences at the University of Michigan, Dearborn. Prof. Koumpias is an applied microeconomist with research interests in public economics, with an emphasis on behavioral tax compliance, and health economics. In his research, he employs quasi-experimental methods to disentangle the causal impact of policy interventions that occur at the aggregate (e.g. states) or the individual (e.g. taxpayers) level in a comparative case study setting. Namely, he relies on regression discontinuity designs, regression kink designs, matching methods, and synthetic control methods to perform program evaluation that estimates the causal treatment effect of the policy in question. Examples include the use of a regression discontinuity design to estimate the impact of a tax compliance reminders on payments of overdue income tax liabilities in Greece, matching methods to measure the influence of mass media campaigns in Pakistan on income tax filing and the synthetic control method to evaluate the long-term effect of state Medicaid expansions on mortality.

Evolution of Annual Changes in All-cause Childless Adult Mortality in New York State following 2001 State Medicaid Expansion

Jinseok Kim

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Jinseok Kim, Ph.D., is Research Assistant Professor in the Institute for Social Research at the University of Michigan, Ann Arbor.  Prof. Kim works on resolving named entity ambiguity in large-scale scholarly data (publication, patent, and funding records) in digital libraries. Especially, his current research is focused on developing methods for disambiguating author and affiliation names at a digital library scale using various supervised machine learning approaches trained on automatically labeled data . Disambiguated data from multiple sources will be integrated to be analyzed for insights into research production, scientific collaboration, funding evaluation, and research policy at a national level.

Brian P. McCall

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My interests are in the areas of labor economics, program evaluation, and the economics of education. Currently my research focuses on college student debt accumulation and the subsequent risk of default, the effect of tuition subsidies on college attendance, the influence of family wealth on college attendance and completion, the effect of financial aid packages on college attendance, completion and subsequent labor market earnings, the influence of education on job displacement and subsequent earnings, the impact of unemployment insurance rules on unemployment durations and re-employment wages, and the determinants and consequences of repeat use of the unemployment insurance system.