Bayesian inference offers a coherent framework for updating beliefs about unknown quantities in light of observed data. At its core lies Bayes’ theorem, which combines a prior distribution, ...

We develop novel methods to make Bayesian inference more efficient, scalable, and practical. This includes work on variational methods, Monte Carlo algorithms, and techniques for handling complex ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

Research areas: Statistics, Machine learning, Bayesian nonparametric models, Fairness in machine learning and statistics, Reinforcement learning, Causal Inference, Conformal Inference. Bayesian ...