I am broadly interested in design and analysis of statistical methods for large-scale data, high-dimensional inference, network analysis, non-convex optimization and personalized decision-making. Some of the topics that I have been working on in the past few years include:

Uncertainty-aware machine learning: Debiasing statical models, Predictive inference, Online hypothesis testing, Model evaluation and calibration

Theoretical understanding of deep learning: Iterative estimation, non-convex optimization, Adversarial robustness, Learning under distribution shift

Personalized decision making: Dynamic pricing and product assortment under feature-based valuation models, robustness against model misspecification, incentive-aware learning with strategic buyers, exploiting global shrinkage models to exploit customers network structure

Private learning: Reidentification attacks, Private causal inference, Aggregate learning, Look-alike clustering