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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.

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

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.

Peter Adriaens

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My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies.  Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.

Adriene Beltz

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The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Nils G. Walter

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Nils G. Walter, PhD, is the Francis S. Collins Collegiate Professor of Chemistry, Biophysics and Biological Chemistry, College of Literature, Science, and the Arts and Professor of Biological Chemistry, Medical School, at the University of Michigan, Ann Arbor.

Nature and Nanotechnology likewise employ nanoscale machines that self-assemble into structures of complex architecture and functionality.  Fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblies in real-time.  In particular, single molecule fluorescence resonance energy transfer (smFRET) allows us to measure distances at the 2-8 nm scale, whereas complementary super-resolution localization techniques based on Gaussian fitting of imaged point spread functions (PSFs) measure distances in the 10 nm and longer range.  In terms of Big Data Analysis, we have developed a method for the intracellular single molecule, high-resolution localization and counting (iSHiRLoC) of microRNAs (miRNAs), a large group of gene silencers with profound roles in our body, from stem cell development to cancer.  Microinjected, singly-fluorophore labeled, functional miRNAs are tracked at super-resolution within individual diffusing particles.  Observed mobility and mRNA dependent assembly changes suggest the existence of two kinetically distinct assembly processes.  We are currently feeding these data into a single molecule systems biology pipeline to bring into focus the unifying molecular mechanism of such a ubiquitous gene regulatory pathway.  In addition, we are using cluster analysis of smFRET time traces to show that large RNA processing machines such as single spliceosomes – responsible for the accurate removal of all intervening sequences (introns) in pre-messenger RNAs – are working as biased Brownian ratchet machines.  On the opposite end of the application spectrum, we utilize smFRET and super-resolution fluorescence microscopy to monitor enhanced enzyme cascades and nanorobots engineered to self-assemble and function on DNA origami.

Artistic depiction of the SiMREPS platform we are building for the direct single molecule counting of miRNA biomarkers in crude biofluids (Johnson-Buck, A. et al. Kinetic fingerprinting to identify and count single nucleic acids. Nat Biotechnol 33, 730-732 (2015)).

Artistic depiction of the SiMREPS platform we are building for the direct single molecule counting of miRNA biomarkers in crude biofluids (Johnson-Buck, A. et al. Kinetic fingerprinting to identify and count single nucleic acids. Nat Biotechnol 33, 730-732 (2015)).

Keshav Pokhrel

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Keshav Pokhrel, PhD, is Assistant Professor of Statistics at the University of Michigan, Dearborn.

Prof. Pokhrel’s research interests include the epidemiology of cancer, time series forecasting, quantile regression and functional data analysis. The skewed and non-normal data are increasingly more frequent than ever before. The data in the extreme ends are of their own importance. Hence the importance of quantile regression. The availability of the information is increasingly functional. My current work is gearing towards functional data analysis techniques such as principal differential analysis which can estimate a system of differential equations to reveal the dynamics of real data.

Jessica K. Camp

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Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.

Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.