Marcelline Harris

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Marcelline Harris, Ph.D., R.N., is Associate Professor of Systems, Populations and Leadership in the School of Nursing at the University of Michigan, Ann Arbor.

Dr. Harris’s research interests focus on what is being labeled the “continuous use” of clinical data (the use of clinical data for one or more purposes), computable knowledge representation strategies, and the use of electronic clinical data for practice and research.  Her research has been funded by NIH, AHRQ, RWJF, and PCORI.  Harris also has extensive enterprise level experience, having served in both scientific and operational positions that address the development and governance of systems that support the capture, storage, indexing, and retrieval of clinical data.  At Michigan, she retains this translational perspective, emphasizing clinical data for patient-centered research, clinical surveillance and predictive analytics.

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.

Kevin Quinn

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Prior to joining the Michigan faculty, Professor Quinn was a Professor of Law at UC Berkeley. His research focuses on questions of empirical legal studies and statistical methodology. His research has been supported by the National Science Foundation and has appeared in leading journals in political science, statistics, and law. Professor Quinn is a former President of the Society for Political Methodology and his research has received multiple professional awards.

Satish Narayanasamy

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Satish Narayanasamy, Ph.D., is Associate Professor in the Electrical Engineering and Computer Science department in the College of Engineering at the University of Michigan, Ann Arbor. Satish’s interests are working at the intersection of computer architecture, software systems and program analysis. His current interests include concurrency, security, customized architectures and tools for mobile and web applications, machine learning assisted program analysis, and tools for teaching at scale.

Bhramar Mukherjee

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Bhramar Mukherjee is  a Professor in the Department of Biostatistics, joining the department in Fall, 2006. Bhramar is also a Professor in the Department of Epidemiology. Bhramar completed her Ph.D. in 2001 from Purdue University. Bhramar’s principal research interests lie in Bayesian methods in epidemiology and studies of gene-environment interaction. She is also interested in modeling missingness in exposure, categorical data models, Bayesian nonparametrics, and the general area of statistical inference under outcome/exposure dependent sampling schemes. Bhramar’s methodological research is funded by NSF and NIH.   Bhramar is involved as a co-investigator in several R01s led by faculty in Internal Medicine, Epidemiology and Environment Health sciences at UM. Her collaborative interests focus on genetic and environmental epidemiology, ranging from investigating the genetic architecture of colorectal cancer in relation to environmental exposures to studies of air pollution on pediatric Asthma events in Detroit. She is actively engaged in Global Health Research.

Jeremy M G Taylor

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I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

Long Nguyen

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I am broadly interested in statistical inference, which is informally defined as the process of turning data into prediction and understanding. I like to work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. My motivation for all this came originally from an early interest in machine learning, which continues to be a major source of research interest. A primary focus of my group’s research in machine learning to develop more effective inference algorithms using stochastic, variational and geometric viewpoints.