My research focuses on decision-making under uncertainty, with an emphasis on developing new theoretical approaches that yield interpretable, practice-relevant insights. To date, I have studied the optimal control of dynamic matching markets, primarily using applied probability and queueing theory. Applications include organ exchange programs, ride-hailing platforms, and perishable inventory systems such as blood and food banks.
These problems can be analyzed using tools from optimization, simulation, and machine learning. Rather than proposing purely analytical solution methods, my work emphasizes uncovering structural properties of optimal control; for example, proving the near-optimal performance of simple, interpretable algorithms and decision rules that can inform practical decision-making.
At Ross, I teach TO 640 Big Data Management: Tools and Techniques.