426 N. Ingalls Street
Ann Arbor, MI 48109-2003
Available to mentor
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).
SOCR Dinov Bio Dinov Pubs SOCR Research DInov GoogleScholar SOCR News
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Ph.D.Florida State University, Tallahassee
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M.S.Florida State University, Tallahassee
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M.S.Michigan Technological University, Houghton
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B.S.Sofia University, Sofia
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Center MemberInstitute for Healthcare Policy and Innovation
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Center Membere-Health and Artificial Intelligence Initiative
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Center MemberCenter for Integrative Research in Critical Care
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Center MemberPrecision Health Initiative
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.
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Cheng K, Shen Y, Dinov ID. Journal of Statistical Theory and Practice, 2024 Sep 1; 18 (3):Journal ArticleApplications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation
DOI:10.1007/s42519-024-00384-5 -
Niraula D, Cuneo KC, Dinov ID, Gonzalez BD, Jamaluddin JB, Jin JJ, Luo Y, Matuszak MM, Ten Haken RK, Bryant AK, Dilling TJ, Dykstra MP, Frakes JM, Liveringhouse CL, Miller SR, Mills MN, Palm RF, Regan SN, Rishi A, Torres-Roca JF, Yu H-HM, El Naqa I. 2024 Apr 30;PreprintIntricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.
DOI:10.1101/2024.04.27.24306434 PMID: 38746238 -
Guha S, Rodriguez-Acosta J, Dinov ID. Neuroinformatics, 2024 Jun 11;Journal ArticleA Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
DOI:10.1007/s12021-024-09670-w PMID: 38861097 -
Dinov ID. Neuroinformatics, 2024 Sep 24;Journal ArticleNeuroinformatics Applications of Data Science and Artificial Intelligence.
DOI:10.1007/s12021-024-09692-4 PMID: 39316274 -
Niraula D, Sun W, Jin J, Dinov ID, Cuneo K, Jamaluddin J, Matuszak MM, Luo Y, Lawrence TS, Jolly S, Ten Haken RK, El Naqa I. Sci Rep, 2023 Mar 31; 13 (1): 5279Journal ArticleA clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).
DOI:10.1038/s41598-023-32032-6 PMID: 37002296 -
Weigard A, McCurry KL, Shapiro Z, Martz ME, Angstadt M, Heitzeg MM, Dinov ID, Sripada C. Transl Psychiatry, 2023 Jun 24; 13 (1): 225Journal ArticleGeneralizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics.
DOI:10.1038/s41398-023-02502-6 PMID: 37355620 -
Abdul Rahman H, Ottom MA, Dinov ID. BMC Cancer, 2023 Feb 10; 23 (1): 144Journal ArticleMachine learning-based colorectal cancer prediction using global dietary data.
DOI:10.1186/s12885-023-10587-x PMID: 36765299 -
Abdul Rahman H, Kwicklis M, Ottom M, Amornsriwatanakul A, H Abdul-Mumin K, Rosenberg M, Dinov ID. Bioengineering (Basel), 2023 May 10; 10 (5):Journal ArticleMachine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.
DOI:10.3390/bioengineering10050575 PMID: 37237644