Research Interests

Theory

Randomness is an intrinsic feature of many real-world systems and decision problems. Consequently, modeling uncertainty in optimization to improve decision-making forms the core of my research. From a broader theoretical perspective, my interests span the following areas:

• Data-Driven Stochastic Optimization
• (Distributionally) Robust Optimization
• Decomposition Algorithms
• Machine Learning and Statistical Modeling
• AI-Enhanced Decision-Making

Applications

My applied expertise spans healthcare analytics, transportation, logistics, and quantitative finance. Specific topics addressed in my current and past research include:

• Routing and Scheduling
• Facility Location
• Operating Room Scheduling
• Humanitarian Logistics
• Portfolio Optimization