My current research focuses on developing optimization algorithms that enhance human-AI collaboration for equitable resource allocation in high-stakes decision-making. By integrating large language models (LLMs) with optimization techniques, I aim to create iterative frameworks that combine algorithmic speed with human expertise to refine policies. Some of my current projects include
Multi-armed bandit algorithms for human-AI collaboration
Intersecting optimization with policymaking in healthcare, energy, technology integration, and resource allocation
Optimization for resource allocation under fairness constraints
Past research topics included sequential decision-making and optimizing equitable wildfire resilience policy.
Check out my papers below!
Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices (Applied Energy). See paper
The implications of state aggregation in deteriorating Markov Decision Processes with optimal threshold policies (Major revision at Computers & OR). See paper
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement LearningSee paper
