Zeina Mneimneh

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Dr. Zeina Mneimneh is Assistant Research Scientist in the University of Michigan Survey Research Center.

Her research focuses on the use of social media and neighborhood contextual information to study social and health science topics and involves a collaboration between Michigan and Georgetown University.

Bryan R. Goldsmith

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Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

Edward G. Happ

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Edward G. Happ is an Executive Fellow at the University of Michigan School of Information, where he is teaching and conducting research. He is also the Co-Founder and former Chairman of NetHope (www.nethope.org), a U.S. based consortium of 50+ leading international relief, development and conservation nonprofits focused on information and communications technology (ICT) and collaboration.

 

Peter Adriaens

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Prof. Adriaens’ research focuses on the use of data science to uncover trends and features in a range of financial (‘fintech’) applications relevant to economic development and investments aimed at catalyzing sustainable growth, including:
1. Network mapping to query relations in financial networks using visualization techniques
2. Trend and features prediction of value capture and investment grade in startup business models, using machine learning, natural language processing, and decision tools
3. Asset risk pricing of stocks exposed to water risk in their supply chains, using statistical methods, and portfolio theory predictions
4. Financial risk modeling of multi-asset investment funds to drive low carbon economies, leveraging network mapping, and machine learning.

 

Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

V.G.Vinod Vydiswaran

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V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.

Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and  laypersons.

Z. Morley Mao

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Z. Morley Mao, PhD, is Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor campus.

Ramon Satyendra

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Ramon Satyendra, PhD, is Associate Professor of Music Theory and Director of Graduate Studies in the School of Music, Theatre & Dance at the University of Michigan, Ann Arbor.

Professor Satyendra holds a doctorate from the University of Chicago in music theory and history. Before coming to Michigan, he taught at Yale University and the University of Chicago. He currently serves on the editorial boards of The Journal of Mathematics and Music, Intégral, and Analytical Approaches to World Music. Highlights of previous service to the field include Executive Committee of the Society of Music Theory, editorial board of Music Theory Spectrum, and editor of the Journal of Music Theory.  Among his awards are the Merten Hasse Award in Mathematics from the Mathematical Association of America and the Clauss Prize for Teaching Excellence in the Humanities from Yale University. He is a three-time fellow of the Mannes Institute for Advanced Studies in Music Theory. Satyendra’s research interests include music and mathematics, late nineteenth-century music, jazz, South Asian music, and compositional theory. He plays piano, organ, tabla, and guitar and has published in Music Theory Spectrum, Music Analysis, Journal of Music Theory, American Mathematical Monthly, and elsewhere.

 

 

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.

Kevin Dombkowski

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Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics in the University of Michigan Medicine. Dr. Dombkowski, also holds a second appointment in the School of Public Health.

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.

Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.

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.