Ivo D. Dinov, PhD

Ivo Dinov

Computational Medicine and Bioinformatics, Medical School
Associate Director for Education and Training, Michigan Institute for Data Science
Department of Health Behavior and Biological Sciences Vice Chair
Department of Health Behavior and Biological Sciences
Room 4126 SNB
University of Michigan School of Nursing
426 North Ingalls Street
Ann Arbor, MI 48109-2003
Ivo Dinov is accepting new PhD students.


  • Spacekime and Predictive healthcare analytics
  • Biomedical data science
  • 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.

Current Research Grants and Programs

  • NS091856 Biostatistics and Data Management Core, Cholinergic Mechanisms of Gait Dysfunction in Parkinson's Disease. This research examines the role of cholinergic lesions in gait and balance abnormalities in Parkinson's Disease and develops novel treatment strategies targeted at cholinergic neurotransmission.
  • DK089503 Integrative Biostatistics and Informatics Core. The Michigan Nutrition Obesity Research Center conducts research to encourage and enable researchers to integrate advanced phenotyping and computational tools to more fully define individual and population characteristics that arise in response to dietary nutrient composition or amount.
  • NR015331 Center for Complexity and Self-management of Chronic Disease investigates health promotion, illness prevention and the burden of chronic illness burgeons using advanced methods, complexity theory, and data analytics.
  • NSF DUE 1023115 The Distributome Project (http://distributome.org/) is an open-source, open content-development project for exploring, discovering, learning, and computational utilization of diverse probability distributions. Role: Site-Principal Investigator.
  • EB020406 Big Data for Discovery Center aims to create a user-focused graphical system to dynamically create, modify, manage and manipulate multiple collections of big datasets and enrich next generation "Big Data" workflow technologies as well as to develop an interface to enable modeling, visualization, and the interactive exploration of Big Data.
  • NSF 1916425: This project builds the Midwest Big Data Hub, a consortium of partners and working groups working in Big Data and including stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefiting society.
  • NIH 1R01CA233487: Optimal Decision Making in Radiotherapy Using Panomics Analytics. The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data. To meet this need and overcome current challenges, we have assembled a multidisciplinary team including: clinicians, medical physicists, data scientists, and human factor experts.


Dr. Dinov’s teaching philosophy has evolved and matured over the past two decades from a concept-based instruction to a more pedagogically balanced approach of integrated research, practice and education. He has taught many core and multidisciplinary classes in statistics, mathematics, neuroscience and psychology. Dr. Dinov is developing active learning materials, web-based computational resources, dynamic databases, blended learning materials and electronic instructional resources. The foci of his ongoing educational research are on increasing learners’ motivation, enhancing the learning experiences and improving knowledge retention. As Director of the Statistics Online Computational Resource (SOCR), Dr. Dinov designs, implements and validates novel virtual experiments, web apps for probability, statistics and informatics education, and introduces new multilingual science, technology, engineering and mathematics (STEM) resources.

Notable Awards / Honors

  • World Wide Web Awards™ "Gold" Award, July 2007
  • IEEE Mathematical Methods in Biomedical Image Analysis (MMBIA) Best Paper Award, 2008
  • Runner up, ASA Hands-On Statistics Activity Competition, 2010


  • 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

Publication Highlights

  • Zhou, Y, Zhao, Zhou, N, Zhao, Yi, Marino, S, Wang, T, Sun, H, Toga, AW, Dinov, ID. (2019). Predictive Big Data Analytics using the UK Biobank Data, Scientific Reports, 9(1): 6012, DOI: 10.1038/s41598-019-41634-y.

  • Dinov, ID. (2019). Flipping the grant application review process, Studies in Higher Education, 1-9, DOI: 10.1080/03075079.2019.1628201.

  • Sta. Cruz, S, Dinov, ID, Herting, MM, González-Zacarías, C, Kim, H, Toga, AW, and Sepehrband, F. (2019). Imputation Strategy for Reliable Regional MRI Morphological Measurements, Neuroinformatics, First Online: 04 May 2019, DOI: 10.1007/s12021-019-09426-x.

  • Ming, C, Viassolo, V, Probst-Hensch, N, Chappuis, PO, Dinov, ID, and Katapodi, MC. (2019) Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models, Breast Cancer Research 21(1):75, DOI: 10.1186/s13058-019-1158-4.

  • Potempa, K, Rajataramya, B, Barton, DL, Singha-Dong, N, Stephenson, R, Smith, EML, Davis, M, Dinov, I, Hampstead, BM, Aikens, JE, Saslow, L, Furspan, P, Sarakshetrin, A, and Pupjain, S. (2019) Impact of using a broad-based multi-institutional approach to build capacity for non-communicable disease research in Thailand, Health Research Policy and Systems, 17:62, DOI: 10.1186/s12961-019-0464-8.

  • Dinov, ID. (2019) Quant data science meets dexterous artistry, International Journal of Data Science and Analytics, 7(2):81–86, DOI: 10.1007/s41060-018-0138-6.

  • Marino, S, Zhou, N, Zhao, Yi, Wang, L, Wu Q, and Dinov, ID. (2019) DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets, Journal of Statistical Computation and Simulation, 89(2): 249–271, DOI: 10.1080/00949655.2018.1545228.

  • Avesani, P, McPherson, B, Hayashi, S, Caiafa, CF, Henschel, R, Garyfallidis, E, Kitchell, L, Bullock, D, Patterson, A, Olivetti, E, Sporns, O, Saykin, JA, Wang, L, Dinov, ID, Hancock, D, Caron, B, Qian, Y, and Pestilli, F. (2019) The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services, Scientific data, 6(1):69, DOI: 10.1038/s41597-019-0073-y.

  • Dinov, ID, Vandervest, J, and Marino, S. 2019. Electronic Medical Record Datasifter, US Patent App. 16/051,881 (US20190042791A1).

  • Dinov, ID, 2018. Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer, Computer Science, ISBN 978-3-319-72346-4.

  • Tang, M., Gao, C, Goutman, SA, Kalinin, A, Mukherjee, B, Guan, Y, and Dinov, ID. (2018) Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering, Neuroinformatics, 1-15, DOI: 10.1007/s12021-018-9406-9.

  • Kalinin, AA, Allyn-Feuer, A, Ade, A, Fon, GV, Meixner, W, Dilworth, D, Husain, SS, de Wett, JR, Higgins, GA, Zheng, G, Creekmore, A, Wiley, JW, Verdone, JA, Veltri, RW, Pienta, KJ, Coffey, DS, Athey, BD, and Dinov, ID. (2018) 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification, Scientific Reports, 8(1): 13658.

  • Marino S, Xu J, Zhao Y, Zhou N, Zhou Y, Dinov, ID. (2018) Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies, PLoS ONE 13(8): e0202674, DOI: 10.1371/journal.pone.0202674.

  • Complete List of Publications: http://www.socr.umich.edu/people/dinov/publications.html