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Ivo D. Dinov

Professor
Health Behavior and Biological Sciences
University of Michigan
United States of America

Biography

Ph.D., The Florida State University, Tallahassee, FL, 1998 M.S., The Florida State University, Tallahassee, FL, 1998 M.S., Michigan Technological University, Houghton, MI, 1993 B.S., Sofia University, Sofia, Bulgaria, 1991

Research Interest

Interests: Predictive Big Data analytics Data science with biomedical and healthcare applications Health and neuroscience informatics Teaching with technology and blended instruction Mathematical modeling and statistical computing Dr. Dinov is the Director of the Statistics Online Computational Resource (SOCR) and is an expert in mathematical modeling, statistical analysis, high-throughput computational processing and scientific visualization of large datasets (Big Data). His applied research is focused on neuroscience, nursing informatics, multimodal biomedical image analysis, and distributed genomics computing. Examples of specific brain research projects Dr. Dinov is involved in include longitudinal morphometric studies of development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s disease, Parkinson’s disease). He also studies the intricate relations between genetic traits (e.g., SNPs), clinical phenotypes (e.g., disease, behavioral and psychological test) and subject demographics (e.g., race, gender, age) in variety of brain and heart related disorders. Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for science education and active learning.

Publications

  • Dinov, ID, Siegrist, K, Pearl, DK, Kalinin, A, Christou, N (2015). Probability Distributome: a web computational infrastructure for exploring the properties, interrelations, and applications of probability distributions. Computational Statistics, 594: 1-19. DOI: 10.1007/s00180-015-0594-6

  • Husain, SS, Kalinin, A, Truong, A, Dinov, ID. (2015) SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information. Journal of Big Data, 2(13):1-18. DOI: 10.1186/s40537-015-0018-z

  • Lederman, C, Joshi, A, Dinov, ID, Van Horn, JD, Vese, L, Toga, A. (2016) A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures. Journal of Mathematical Imaging and Vision, 55(2)179-198.

  • Dinov, ID. (2016) Volume and Value of Big Healthcare Data. Journal of Medical Statistics and Informatics, 4(3)1-7 DOI: 10.7243/2053-7662-4-3

  • Dinov, ID. (2016) Methodological challenges and analytics opportunities for modeling and interpreting Big Healthcare Data. GigaScience, 5(12) 1-15, DOI:10.1186/s13742-016-0117-6

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