Research Interests
Statistical Inference for Stochastic Differential Equations
My primary research interest lies in statistical inference for stochastic differential equations, particularly multiscale stochastic dynamical systems. I work on developing probabilistic and computational methods for learning effective dynamics from partially observed stochastic systems. My recent work focuses on Bayesian approaches and normalizing-flow-based methods for inference in averaged stochastic models.
Gaussian Mixture Problems
I am also interested in problems involving Gaussian mixtures, including distribution learning, density estimation, and computational aspects of high-dimensional mixture models.
Bayesian and Distributional Reinforcement Learning
More broadly, I am interested in Bayesian reinforcement learning and distributional reinforcement learning, particularly from the perspective of uncertainty quantification and probabilistic modeling in sequential decision-making problems.
Publications and Preprints
Preprints and Submitted Papers
- Learning multiscale stochastic models through normalizing flows. Anan Saha and Arnab Ganguly. Submitted, under review. arXiv:2605.09718
Working Papers
- Neural Reversible-Jump MCMC for Integral-Drift Stochastic Differential Equations. Manuscript in preparation.