My research lies at the intersection of statistical machine learning, Bayesian deep learning, and probabilistic modeling. I develop scalable and interpretable Bayesian methods by combining principled uncertainty quantification with modern deep learning.

My interests include survival analysis, temporal and event-based modeling, hierarchical Bayesian models, and generative modeling, with applications in healthcare, epidemiology, and other data-rich domains. More broadly, I am interested in learning conditional distributions and developing robust methods for prediction and decision-making under uncertainty.