Ivo D. Dinov, Ph.D.

Ivo Dinov

Professor
Henry Philip Tappan Collegiate Professor
Computational Medicine and Bioinformatics, Medical School
Associate Director for Education and Training, Michigan Institute for Data Science
Department of Health Behavior and Biological Sciences
Department of Systems, Populations and Leadership Chair
Department of Systems, Populations and Leadership
Room 4126 NURS2
University of Michigan School of Nursing
426 North Ingalls Street
Ann Arbor, MI 48109-2003
 
Ivo Dinov is accepting new PhD students.

Interests

  • 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.

Teaching

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

Education

  • Postdoc, University of California, Los Angeles, CA, 2001
  • 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

  • Dinov, ID. (2023). Data Science and Predictive Analytics: Biomedical and Health Applications using R, 2nd edition, Springer Series in Applied Machine Learning, ISBN 978-3-031-17482-7.

  • Ottom, MA, Abdul Rahman, H, Alazzam, IM and Dinov, ID. (2023). Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet, Bioengineering, 10(5):581, DOI: 10.3390/bioengineering10050581.

  • Moon, SW, Zhao, L, Matloff, W, Hobel, S, Berger, R, Kwon, D, Kim, J, Toga, AW, and Dinov, ID. (2023) Brain structure and allelic associations in Alzheimer's disease, CNS Neuroscience & Therapeutics, 29:1034-1048, DOI: 10.1111/cns.14073. 

  • Niraula, D, Sun, W, Jin, J, Dinov, ID, Cuneo, K, Jamaluddin, J, Matuszak, MM, Luo, Y, Lawerence, TS, Jolly, S, Haken, RKT, and El Naqa, I. (2023). A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCLiDS), Sci Rep 13, 5276, DOI: 10.1038/s41598-023-32032-6.

  • Abdul Rahman, H, Kwicklis, M, Ottom, M, Amornsriwatanakul, A, H Abdul-Mumin, K, Rosenberg, M, and Dinov, ID. (2023). Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students, Bioengineering, 10(5):575, DOI: 10.3390/bioengineering10050575.

  • Abdul Rahman, H, Ottom, MA, and Dinov, ID. (2023) Machine learning-based colorectal cancer prediction using global dietary data, BMC Cancer, 23(144):1-13, DOI: 10.1186/s12885-023-10587-x

  • Wang, Y, Shen Y, Deng, D, Dinov, ID. (2022) Determinism, Well-posedness, and Applications of the Ultrahyperbolic Wave Equation in Spacekime, Journal of Partial Differential Equations in Applied Mathematics, 5(100280), DOI: 10.1016/j.padiff.2022.100280.

  • Bobrovnikov, M, Chai, JT, and Dinov, ID. (2022) Interactive Visualization and Computation of 2D and 3D Probability Distributions, SN Computer Science, 3, 327, DOI: 10.1007/s42979-022-01206-w.

  • Zhou, N, Wu, Q, Wu, Z, Marino, S, Dinov, ID. (2022) DataSifterText: Partially Synthetic Text Generation for Sensitive Clinical Notes, Journal of Medical Systems, 46(96):1-14, DOI: 10.1007/s10916-022-01880-6.

  • Zhang, R, Zhang, Y, Liu, Y, Guo, Y, Shen, Y, Deng, D, Qiu, Y, Dinov, ID. (2022) Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data, Neural Computing and Applications Journal, 34:6377–6396, DOI: 10.1007/s00521-021-06789-8.

  • Kalinin, AA, Palanimalai, S, Zhu, J, Wu, W, Devraj, N, Ye, C, Ponaru, N, Husain, SS, and Dinov, ID. (2022) SOCRAT: A Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualizatino, Journal Information, 13(547):1-24, DOI: 10.3390/info13110547.

  • Yamada, C, Edelson, MF, Lee, AC, Saifee, NH, and Dinov, ID. (2022) Transfusion-associated hyperkalemia in pediatric population: Analyses for risk factors and recommendations, Transfusion, 62(12):2503-2514, DOI: 10.1111/trf.17135.

  • Zhou, N, Wang, L, Marino, S, Zhao, Y, Dinov, ID. (2022) DataSifter II: Partially Synthetic Data Sharing of Sensitive Information Containing Time-varying Correlated Observations, Journal of Algorithms & Computational Technology, 15:1–17, DOI: 10.1177/17483026211065379.

  • Dinov, ID and Velev, MV. (2021) Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics, De Gruyter, STEM Series, ISBN 978-3-11-069780-3, DOI 10.1515/9783110697827.

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