Ivo Dinov
Ivo Dinov
dinov@umich.edu
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Years
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Institution
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Role/Degree
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Field
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2019
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University of Michigan, Ann Arbor
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Associate Director, NGP
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Computational Neuroscience
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2015
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University of Michigan, Ann Arbor
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Associate Director,
Michigan Institute for Data Science
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Data Science
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2013-Present
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University of Michigan, Ann Arbor
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Faculty
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Health Behavior and Biological Science, Computational Medicine and Bioinformatics, Data Science
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2001-2013
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University of California, Los Angeles
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Faculty
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Statistics and Neurology
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1998-2001
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UCLA School of Medicine
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Postdoctoral Fellow
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Computational Neuroscience and Brain Mapping
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1993-1998
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Florida State University
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PhD/MS
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Mathematics/Statistics
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1993-1998
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Florida State University
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Pre-doctoral fellow
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Industrial engineering/Optimization
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1991-1993
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Michigan Technological University
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MS
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Mathematics
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1987-1991
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University of Sofia, Bulgaria
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BS
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Mathematics and Informatics
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Complete details on ongoing research projects are available online: http://www.socr.umich.edu/people/dinov/research.html
Prof. Dinov conducts research in computational and data science, artificial intelligence and statistical learning, health analytics, neuroscience and brain mapping, mathematical modeling, statistical inference, bioinformatics, and STEM education.
There are a number of challenges, opportunities, and strategies for designing, collecting, managing, processing, interrogating, analyzing and interpreting complex datasets. We develop, validate and share methods, software tools, and protocols that can be applied to a broad spectrum of Big Data problems. This includes building mathematical foundations, computational statistics algorithms, and modern scientific inference techniques to model, visualize and interpret heterogeneous biomedical data.
The SOCR Lab is involved in challenging neuroscience projects examining brain development, maturation and aging in health and disease. Specific projects include studying normal and pathological pediatric development (e.g., Autism, ADHD, Schizophrenia), memory decline and dementia (e.g., Alzheimer's disease), and various other brain related disorders (e.g., ALS, Parkinson's disease).
SOCR also develops interactive learning modules, dynamic instructional resources, and technology-enhanced educational resources (e.g., MIDAS Graduate Data Science Certificate Program, Graduate Health Analytics Curriculum. Our team works on integration of cognitive, genetics, phenotypic, imaging, and biospecimen data requires novel strategies to represent high-dimensional and incongruent data as computable data objects. This research project aims to develop effective informatics techniques that address this difficult challenge using a scalable, reproducible, and reliable computational workflow environment (e.g., Pipeline workflows).
Complete list of publications is available online:
(in press) 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.
Dinov, ID. (2020). Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs, Frontiers in Public Health, 8(22):1-10, DOI: 10.3389/fpubh.2020.00022.
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, 18, 59–70, 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.
Zhao, L. Matloff, W, Ning, K, Kim, H, Dinov, ID, and Toga AW. (2018) Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults, Cerebral Cortex, advance preprint, bhy300, DOI: 10.1093/cercor/bhy300.
Zheng G, Kalinin AA, Dinov, ID, Meixner W, Zhu S, Wiley JW. (2018) Hypothesis: Caco‐2 cell rotational 3D mechanogenomic turing patterns have clinical implications to colon crypts, J Cell Mol Med. 2018;00:1–6, DOI: 10.1111/jcmm.13853.
Gao C, Sun H, Wang T, Tang M, Bohnen NI, Müller MLTM, Herman, T, Giladi, N. Kalinin, A, Spino, C, Dauer, W, Hausdorff, JM, Dinov, ID. (2018) Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease, Scientific Reports, 8(1):7129. doi: 10.1038/s41598-018-24783-4 2018.
Dinov, ID, Palanimalai, S, Khare, A, and Christou, N. (2018) Randomization‐based Statistical Inference: A resampling and simulation infrastructure, Teaching Statistics, 40: 64–73. DOI: 10.1111/test.12156.
Sepehrband, F., Lynch, K.M., Cabeen, R.P., González-Zacarías, C., Zhao, L., D’Arcy, M., Kesselman, C., Herting, M.M., Dinov, I.D., Toga, A.W., Clark, K.A., 2018. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning, NeuroImage, 172:217–227, DOI: 10.1016/j.neuroimage.2018.01.065.
Kalinin, AA, Higgins, GA, Reamaroon, N, Soroushmehr, SM, Allyn-Feuer, A, Dinov, ID, Najarian, K, Athey, BD. (2018). Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification, Pharmacogenomics 19:7, 629-650.
The Statistics Online Computational Resource (SOCR): https://SOCR.umich.edu and http://SOCR.ucla.edu.