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 Assistant 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.
Brian Arbic is an Associate Professor in the Department of Earth and Environmental Sciences, with an appointment in the Department of Climate and Space Sciences Engineering and affiliations with Applied and Interdisciplinary Mathematics, Applied Physics, and the Center for the Study of Complex Systems. Arbic is a physical oceanographer primarily interested in the dynamics and energy budgets of oceanic mesoscale eddies (the oceanic equivalent of atmospheric weather systems), the large-scale oceanic general circulation, and tides. He has also studied paleotides, tsunamis, and the decadal variability of subsurface ocean temperatures and salinities. His primary tools are numerical models of the ocean. Arbic uses both realistic models, such as the HYbrid Coordinate Ocean Model (HYCOM) being used as a U.S. Navy ocean forecast model, and idealized models. He frequently compares the outputs of such models to oceanic observations, taken with a variety of instruments. Comparison of models and observations helps us to improve models and ideas about how the ocean works. His research has often been interdisciplinary, involving collaborations with scientists outside of my discipline, such as glaciologists, geodynamicists, and marine geophysicists.