Ming Lin’s research focuses on high dimensional high order statistics and the related applications in real world machine learning problems. His recent research topics includes symmetric matrix sensing, Positive Unlabeled learning, One-bit Active learning and nonconvex tensor machine.
- Preventions against cognitive decline and dementia (pharmacological and behavioral)
- Early detections of Alzheimer’s Disease/ Normal cognitive aging
- Longitudinal data analysis
- Epidemiology of dementia and mild cognitive impairment
- Cross national comparisons on factors associated with healthy cognitive aging
- Application of demographic methods to clinical research
- Social Epidemiology
- BA, Tokyo Woman’s Christian University, Tokyo, Japan
- MA, Demography and Statistics, Pennsylvania State University, State College, PA
- Ph.D., Demography and Statistics, Pennsylvania State University, State College, PA
Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).
Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.
Dr. Wen’s current research interests include topics in Bayesian model comparison, Bayesian multiple hypothesis testing and probabilistic graphical models. In applied field, he is particularly interested in seeking statistically sound and computationally efficient solutions to scientific problems in areas of genetics and functional genomics.
Dr. Gonzalez studies judgment and decision making processes at both the basic and applied levels. His theoretical work includes formal models of decision making under risk and uncertainty. His applied work in decision making extends to eyewitness identification, medical decision making, consumer behavior, transportation decisions and sustainability. He also conducts mathematical modeling of group processes and develops statistical techniques for data analytic problems in psychology. He has developed statistical models for the analysis of dyadic data. Gonzalez teaches graduate-level statistics courses and directs the Biosocial Methods Collaborative. He has been at University of Michigan’s Psychology department since 1997, with joint appointments in Statistics and Marketing. He is a Research Professor at the Research Center for Group Dynamics as well as the Center for Human Growth and Development. He is also a Faculty Associate of the UM Comprehensive Cancer Clinic and the Center for Computational Medicine and Bioinformatics. He co-founded and co-directed with Panos Papalambros the Design Science Program at the University of Michigan. He is currently director of the Biosocial Methods Collaborative at the Institute of Social Research.
Cynthia Chestek is an Associate Professor of Biomedical Engineering, Electrical Engineering – Electrical and Computer Engineering Division, and the Neurosciences Graduate Program.
Martin Swany is Deputy Director of the Center for Research in Extreme Scale Technologies (CREST) at the Indiana University in Bloomington. His research interests include high-performance parallel and distributed computing and networking.
Shawn McKee is a Research Scientist in the Department of Physics, and the Director of MICDE’s Center for Network and Storage-Enabled Collaborative Computational Science.
He is also the U-M site director for ATLAS Great Lakes Tier 2, which provides 4,000 CPUs cores and 3.5 petabytes of storage for ATLAS physics computing. McKee’s research interests are mainly in two parts: using the ATLAS detector to search for Dark-Matter (assuming it has a particle physics origin; and researching distributed data-intensive infrastructures to improve their ability to support high-energy physics and similar distributed e-Science efforts.
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.