At LinkedIn, I work with a team of exceptional researchers and engineers to integrate a long-term reward perspective into our AI systems. My focus is on developing and building systems for return-conditioned supervised learning-based reinforcement learning (e.g., Decision Transformer), uncertainty quantification, contextual bandits, and offline replay. I am particularly interested in creating algorithms that provide reliable uncertainty quantification and enhance the efficiency of exploration.

My statistics research centers around semiparametric/nonparametric methodology and large sample theory - efficient estimation in semiparametric models, nonparametric function estimation (with special emphasis on shape constrained estimation), likelihood and bootstrap based inference in (non-standard) parametric and nonparametric models. The main motivation of the research is in developing nonparametric procedures that are automated (free from tuning parameters) but still flexible enough to incorporate data-driven features.

My research has applications in broad areas such as genetics (multiple testing problems), economics (utility and production function estimation and binary response models), causal inference (conditional independence) and astronomy (analysis of accretion of galaxies), among other fields.

Outside of statistics, I enjoy hiking and playing tennis. I am originally from the city of Bhubaneswar in the state of Odisha, India.