Dipankar Bandyopadhyay, PhD

      Professor, Department of Biostatistics

     School of Public Health

          Virginia Commonwealth University

I am a professor in the Department of Biostatistics, School of Public Health, at the Virginia Commonwealth University (VCU). I also serve as the Sr. Advisor of Data & Cancer Modeling, and Core Member, Biostatistics Shared Resources, at the VCU Massey Comprehensive Cancer Center, and the Director of Data Analytics at the VCU Kidney Disease and Transplant Epidemiology (KiTE) core. 

My primary research interests are in spatial/spatio-temporal modeling, survival/time-to-event analysis, Bayesian methodology, non-Gaussian regression, tensor regression, non-Euclidean regression, correlated/longitudinal data, nonparametric methods, precision health, and also their applications to problems in epidemiology and behavioral studies. I usually search for a pragmatic solution to my statistical modeling problems, which can either follow the Bayesian paradigm, or rely on frequentism. In recent times, I am continuing my excitement to work on applications of neural networks, deep learning, and transfer learning methodologies to electronic health records and observational data. My primary clinical interest is in oral and craniofacial epidemiology, in particular, investigating periodontal health and caries progression via studies mostly funded by the NIH/NIDCR. Other clinical research interests include cancer, organ transplant epidemiology (mostly, kidney), substance abuse and alcohol addiction, medical imaging, and neurological disorders.   

I am an elected Fellow of the American Statistical Association (2018), elected member of the International Statistical Institute (2019), and elected Fellow of the American Association for the Advancement of Science (2024). You may find my NIH RePORTER profile here

Representative publications: 

1. Liu Q, Wang S, Bai R and Bandyopadhyay D. (2026). A monotone single-index modal regression powered by deep neural networks for non-Gaussian periodontal data, Biostatistics, 27(1), kxag040

2. Das S, Chae M, Pati D and Bandyopadhyay D. (2025). A monotone single-index model for spatially-referenced multistate current status data, Biometrics, 81(3), ujaf105 

3. Yan L, Zhang X, Lan Z, Bandyopadhyay D and Wu Y. (2025). Variable screening and spatial smoothing in Fréchet regression with application to diffusion tensor imaging, The Annals of Applied Statistics, 19(1), 655-679

4. Lee CY, Wong KY, Lam KF and Bandyopadhyay D. (2023). A semiparametric joint model for cluster size and subunit-specific interval-censored outcomes, Biometrics, 79(3), 2010-2022

5. Guan Q, Reich BJ, Laber E and Bandyopadhyay D. (2020). Bayesian nonparametric policy search with applications to periodontal recall intervals, Journal of the American Statistical Association - Applications & Case Studies,  105(531), 1066-1078  [Won ASA's 2021 award for Outstanding Statistical Application]

6. Bandyopadhyay D and Canale A. (2016). Nonparametric spatial models for clustered ordered periodontal data, Journal of the Royal Statistical Society - Series C, 65(4), 619-640 

7. Reich BJ, Bandyopadhyay D and Bondell H. (2013). A nonparametric spatial model for periodontal data with non-random missingness, Journal of the American Statistical Association - Applications & Case Studies, 108(503), 820-831