I am interested in developing scalable, structured, and interpretable Bayesian methods, with a focus on Bayesian nonparametrics. These often involve building probabilistic models, with particular interests being:

(1) incorporating prior information relevant to a specific problem of interest,

(2) bypassing existing computational bottlenecks, and

(3) obtaining provable theoretical guarantees.

As a part of the OHDSI community, I am passionate about delivering reliable large-scale observational data driven inference to help clinical decision-making in the face of uncertainty.

Preprints

1. Khera, R., Aminorroaya, A., Dhingra, L.S., Thangaraj, P.M., Camargos, A.P., Bu, F., Ding, X., Nishimura, A., Anand, T.V., Arshad, F. and Blacketer, C., Chai, Y., Chattopadhyay, S. et al. (2024). Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. medRxiv, pp.2024-02. (Link)
2. Chattopadhyay, S., Zhang, A. R., & Dunson, D. B. (2024). Blessing of dimension in Bayesian inference on covariance matrices. arXiv preprint arXiv:2404.03805. (Link)

Publications

1. Maitre, L., Guimbaud, J. B., Warembourg, C., Güil-Oumrait, N., Petrone, P. M., Chadeau-Hyam, M., … & Exposome Data Challenge Participant Consortium. (2022). State-of-the-art methods for exposure-health studies: results from the exposome data challenge event. Environment International. (Link)
2. Chattopadhyay, S., Chakraborty, A., & Dunson, D. B. (2023). Nearest Neighbor Dirichlet Mixtures. Journal of Machine Learning Research. (Link)
3. Chattopadhyay, S., Engel, S. M., & Dunson, D. (2024, Just Accepted, Annals of Applied Statistics). Inferring Synergistic and Antagonistic Interactions in Mixtures of Exposures. arXiv preprint arXiv:2210.09279. (Link)