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.
Brian Arbic is a physical oceanographer. His group focuses on global modeling of internal tides and gravity waves, with growing interests in air-sea interactions and modeling of surface tides and their role in Earth System processes over geological time scales. Other interests include the dynamics and energy budgets of oceanic mesoscale eddies (the oceanic equivalent of atmospheric weather systems), tsunamis, and paleotsunamis. His group uses in-situ and remotely sensed observations, idealized models, and realistic models. He collaborates widely with scientists in the US and abroad, and his projects include collaborations with scientists at large modeling centers, such as the US Naval Research Laboratory (NRL), NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL), DOE’s Los Alamos National Laboratory (LANL), Europe’s Mercator Modeling Center, and NASA’s Jet Propulsion Laboratory (JPL). He participates in NASA missions, including the Surface Water Ocean Topography (SWOT) mission, the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) mission, and the Ocean Surface Topography mission. Arbic has been a member of the U-M ASC STEM Africa committee since 2012. He is the principal founder of the Coastal Ocean Environment Summer School in Ghana (https://coessing.org), is the lead on the concept note for “An Ocean Corps for Ocean Science” (https://globaloceancorps.org)