Research

I work on developing principled statistical and algorithmic methods for solving practical problems in machine learning and data science. In particular, I have worked on the following topics:

• Adaptive Decision making problems: Bayesian Optimization, Active Learning and Reinforcement Learning.
• Resource Allocation problems: learning probability distributions, learning MDPs, fair ML.
• Nonparametric statistical inference: permutation-free tests, sequential tests and changepoint detection, uncertainty quantification.