Ivo Dinov, PhD

Ivo Dinov
Henry P Tappan Collegiate Professor of Nursing
Professor of Nursing
Director Academic Program
School of Nursing
Professor of Computational Medicine and Bioinformatics
Medical School
Office:
University of Michigan
Statistics Online Computational Resource (SOCR)
426 N. Ingalls Street
Ann Arbor, MI 48109-2003
Email:
[email protected]
Available to mentor
Ivo Dinov, PhD
Ivo Dinov
Professor
  • About
  • Links
  • Qualifications
  • Center Memberships
  • Research Overview
  • Recent Publications
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  • About

    Ivo D. Dinov is the Henry Philip Tappan Collegiate Professor at the University of Michigan. He is SOCR Director and professor of Health Behavior and Biological Sciences, and Computational Medicine and Bioinformatics. Dr. Dinov is an expert in mathematical modeling, complex time (kime) and spacekime theory, statistical analysis, computational processing, scientific visualization of large datasets (Big Data), and predictive ML/AI health analytics. His applied research is focused on the STEM foundations of artificial intelligence, biomedical informatics, multimodal biomedical image analysis, and distributed genomics computing. Dr. Dinov is a member of the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and a core member of the University of Michigan Comprehensive Cancer Center. He is an elected member of the International Statistical Institute (ISI).

    Links

    • SOCR
    • Dinov Bio
    • Dinov Pubs
    • SOCR Research
    • DInov GoogleScholar
    • SOCR News

    Qualifications

    • Ph.D.
      Florida State University, Tallahassee, United States
    • M.S.
      Florida State University, Tallahassee, United States
    • M.S.
      Michigan Technological University, Houghton, United States
    • B.S.
      Sofia University, Sofia, Bulgaria

    Center Memberships

    • Center Member
      Weil Institute for Critical Care Research
    • Center Member
      Institute for Healthcare Policy and Innovation
    • Center Member
      e-Health and Artificial Intelligence Initiative
    • Center Member
      Center for Global Health Equity
    • Center Member
      AI and Digital Health Innovation

    Research Overview

    In addition to his core STEM scholarly activities, specific artificial intelligence (AI) research projects led by Dr. Dinov include spacekime analytics using 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 methods (e.g., spacekime analytics) and technologies (e.g., CBDA, DataSifter, TCIU) for mathematical modeling, statistical computing, biomedical applications, scientific education, and active learning.

    Recent Publications

    See All Publications
    • Preprint
      The Decoherence Exponent: Stable Phase Noise and Constraints on Objective State Reduction
      Velev MV, Dinov ID. 2026 Aug 21; arXiv,
    • Journal Article
      Model-based equitable learning framework for high-dimensional biomedical data
      Laoye VE, Dinov ID. APS Open Science, 2026 Jul 18; 1: 000084 DOI:10.1103/dgj9-5fck
    • Preprint
      Kime-Representation Formulations of Three Open Problems in the Foundations of Classical Mechanics: Uncertainty, Invariant Entropy, and Directional Degrees of Freedom
      Dinov ID. 2026 Jul 11; arXiv, DOI:10.48550/arxiv.2607.07851
    • Journal Article
      Dirac fields on(Formula presented) (Formula presented)-dimensional spacetime with compact bi-time, kime coordinates, and stochastic kime-phase
      Velev MV, Dinov ID. Physica Scripta, 2026 Jun 1; 101 (23): DOI:10.1088/1402-4896/ae7778
    • Preprint
      Multiview Graph Fusion with Covariates
      Guha S, Rodriguez-Acosta J, Dinov I. 2026 Mar 25; arXiv, DOI:10.48550/arxiv.2603.22215
    • Preprint
      Mouse Bio-behavioral Phenotyping Using a Digital Homecage Framework for Long-timescale, High-resolution, and Multi-factor Data Collection and Analytics.
      Ognjanovski N, Ghimire A, Hale PJ, Kim DS, Cerda IH, Goldiez E, Marino S, Green A, Fitzgerald PJ, Vijayakumar P, Kirca D, Tong M, Muscat N, Knopf K, Cook M, Tang M, Chen Y, Junior LSB, Weston R, Liu T, Hartner JP, Dinov ID, Watson BO. 2026 Apr 17; DOI:10.21203/rs.3.rs-9152533/v1
      PMID: 42040941
    • Preprint
      Machine learning reveals two key dimensions of developmental risk for substance use disorders in young adulthood
      Weigard AS, Hicks B, Molloy MF, Paige K, McCurry K, Cope L, Hardee J, Martz M, Sripada C, Zucker R, Dinov I, Heitzeg M. 2026 Apr 16; PsyArXiv, DOI:10.31234/osf.io/g46nh_v1
    • Journal Article
      Adaptive Coping and Caregiving Self-Efficacy Among Black Family Caregivers of Persons with Dementia
      Robinson-Lane S, Qin L, Johnson F, Dinov I, Giordani B. Innovation in Aging, 2026 Jun 11; 9 (Supplement_2): igaf122.521 - igaf122.521. DOI:10.1093/geroni/igaf122.521

    Featured News & Stories

    Yueyang Shen, PhD
    Department News

    Meet Dr. Yueyang Shen, a DCMB recent PhD graduate

    Thursday, May 15, 2025, Yueyang Shen defended his Ph.D. dissertation titled “Complex time representation and observability of repeated measurement processes with applications of spacekime analytics.” His mentor was professor Ivo Dinov.