12/4/25 | 4:15pm | E51-145
Michael Lingzhi Li
Assistant Professor of Business Administration
Harvard Business School
Abstract: We study the problem of allocating drones to deliver automated external defibrillators (AEDs) for out-of-hospital cardiac arrests (OHCAs). A key challenge is that the time between the onset of cardiac arrest and the emergency call is unobservable and varies across locations, yet it plays a critical role in determining patient survival. This uncertainty implies that the survival rate curve at each demand location is not known a priori and must be learned through deployment. At the same time, policy decisions must maximize the number of lives saved in real time. This creates a fundamental exploration–exploitation tradeoff, where the planner must learn local survival dynamics while simultaneously optimizing rescue outcomes. We show that the problem can be formulated as a combinatorial multi-armed bandit (CMAB) with latent structural dependencies. Existing CMAB algorithms fail to fully exploit the underlying spatial and operational structure of the allocation problem. We develop a new learning and optimization algorithm that integrates stochastic optimization principles with online learning to achieve near-optimal regret scaling. Computational experiments on large-scale, data-driven OHCA instances demonstrate that our approach reduces cumulative regret by over 20 percent compared to the best performance achieved by common bandit algorithms such as UCB, epsilon-greedy, and Thompson sampling, while remaining computationally efficient and scalable.
Bio: Michael Lingzhi Li is an Assistant Professor in the Technology and Operations Management unit at Harvard Business School. His research focuses on the end-to-end development of decision algorithms based on machine learning, causal inference and operations research. He examines the implementation of such algorithms in high-stakes decision-making, with a focus on healthcare applications. He is the recipient of awards including the Edelman Laureate, the Pierskalla Award, and the Innovative Applications in Analytics Award.