About

I work at the intersection of AI and optimization for online constrained sequential decision making under uncertainty.

In my Ph.D., I focus on solver-in-the-loop and predict-then-optimize approaches for stochastic MPC and multistage stochastic optimization, with interests spanning contextual optimization and decision-focused learning. More broadly, I study how ML components can refine optimization objectives, constraints, and uncertainty models to improve downstream decisions while preserving feasibility and interpretability.

During my M.S., I worked on multivariate time-series forecasting, anomaly detection, and sequential modeling.