Research Interest

• Latent variable models

• Bayesian inference and theory

• Topic models

• Scalable algorithms

• Spatio-temporal data

• Bayesian nonparametrics

• Bandits

• Differential privacy

Dissertation: 

Title: Exploring Interpretable Latent Structure in Modern Data by Bayesian Modeling: Theory and Applications

Advised by: Dr. XuanLong Nguyen

Publications and Preprints

1. Sunrit Chakraborty*, XuanLong Nguyen (2026) Learning Mixtures of Nonparametric and Convolutional Measures on Effectively Low-dimensional Affine Spaces (arxived - to be submitted Statistics journal)

2. Soham Bakshi*, Sunrit Chakraborty (2026) From Collapse to Improvement: Statistical Perspectives on the Evolutionary Dynamics of Iterative Training on Contaminated Sources (arxived - to be submitted ML conference)

3. Sunrit Chakraborty*, Rayleigh Lei*, XuanLong Nguyen (2025) Learning Topic Hierarchies by Tree-Directed Latent Variable Models (Bernoulli, in press)

4. Dat Do*, Sunrit Chakraborty*, Jonathan Trehorst, XuanLong Nguyen (2026) Dirichlet Moment Tensors and the Correspondence between Admixture and Mixture of Products Models (Annals of Statistics, under review)

5. Saptarshi Roy*, Sunrit Chakraborty*, Debabrota Basu (2024). FLIPHAT: Joint Differential Privacy for High-Dimensional Sparse Linear Bandits (The 28th International Conference on Artificial Intelligence and Statistics)

6. Sunrit Chakraborty*, Saptarshi Roy*, Ambuj Tewari (2023). Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits (Proceedings of the 40th International Conference on Machine Learning)

7. Sunrit Chakraborty*, Aritra Guha, Rayleigh Lei, XuanLong Nguyen (2023). Scalable nonparametric Bayesian learning for dynamic velocity fields (Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)