Ming Lin

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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.


Ivo Dinov

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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.



Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.


Barry Grant

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Barry Grant is an Assistant Professor in the Department of Computational Medicine & Bioinformatics. The Grant Lab uses computational approaches, based on both biophysics and bioinformatics, to study the structure, function and evolution of biological macromolecules. We are particularly interested in nature’s nanomachines: molecular motors and switches, which lie at the heart of biological processes, from the division and growth of cells to the muscular movement of organisms. A major portion of our research is focused on deciphering how these fascinating proteins work, and how to manipulate them for industrial and medical advantage.


Santiago Schnell

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Santiago Schnell’s lab combines chemical kinetics, molecular modeling, biochemical measurements and computational modeling to build a comprehensive understanding of proteostasis and protein forlding diseases. They also investigate other complex physiological systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole.

Representation of the human protein-protein interaction network showing disordered (yellow) and ordered (blue) proteins.

Representation of the human protein-protein interaction network showing disordered (yellow) and ordered (blue) proteins.