Broadly, I am interested in scalable and flexible (bio)statistical and machine learning approaches for design and analysis of biomedical studies and their applications to medical or social science and public policy. I use Bayesian methods as well as classical semi- and non-parametric approaches. I love challenges in statistical computing and spend most of my time rubber-ducking my code to myself.
My current research interests include:
- Information-theoretic framework for association and causality: development of statistical methods for studying association and causality without relying on traditional causal inference assumptions, with applications in areas like mediation analysis and instrumental variables.
- Compartmental models for infectious disease modeling: development of spatiotemporal forecasting techniques to study transmission and fallout of infectious diseases, with specific emphasis on COVID-19.
2014 - 17 B.Sc. (Hons.), St. Xavier’s College (Autonomous), Kolkata, WB, INDIA
2017 - 19 M.Stat. (Specialization in Biostatistics), Indian Statistical Institute, Kolkata, WB, INDIA
2019 - 21 M.S. in Biostatistics, University of Michigan, Ann Arbor, MI, USA
2019 - 24 Ph.D. in Biostatistics, University of Michigan, Ann Arbor, MI, USA
(+: co-first author)
- Zhou, Y., Wang, L., Zhang, L., Shi, L., Yang, K., He, J., Bangyao, Z., Overton, W., Purkayastha, S., & Song, P. (2020). A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States. Harvard Data Science Review, (Special Issue 1).
- Purkayastha, S., Bhattacharyya, R., Bhaduri, R., Kundu, R., Gu, X., Salvatore, M., Ray, D., Mishra, S. and Mukherjee, B., 2021. A comparison of five epidemiological models for transmission of SARS-CoV-2 in India. BMC infectious diseases, 21, pp.1-23.
- Purkayastha, S., Kundu, R., Bhaduri, R., Barker, D., Kleinsasser, M., Ray, D. and Mukherjee, B., 2021. Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India. BMC research notes, 14, pp.1-7.
- Salvatore, M.+, Purkayastha, S.+, Ganapathi, L., Bhattacharyya, R., Kundu, R., Zimmermann, L., Ray, D., Hazra, A., Kleinsasser, M., Solomon, S. and Subbaraman, R., 2022. Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience. Science advances, 8(24), p.eabp8621.
- Purkayastha, S. and Song, P.X.K., 2024. fastMI: A fast and consistent copula-based nonparametric estimator of mutual information. Journal of Multivariate Analysis, 201, p.105270.
Open-Source Software
- Bhaduri R., Kundu R., Purkayastha S., Beesley L., Mukherjee B., Kleinsasser, M. 2021. SEIRfansy: Extended Susceptible-Exposed-Infected-Recovery Model
- Purkayastha, S. and Song, P.X.K., 2024. fastMI: A fast and consistent copula-based nonparametric estimator of mutual information.
- Purkayastha, S. and Song, P.X.K., 2024. comet: Collider-mediator testing using information theory.
