Research
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Preprint (ongoing work) | arXiv:2412.06936
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking.
Fast and the Furious: Hot Starts in Pursuit-Evasion Games
AFRL Research Project | 2024 | Accepted to ARMS (Autonomous Robots and Multirobot Systems) Workshop at AAMAS 2025
Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph Characteristic Space is constructed via multi-objective optimization to identify Pareto-optimal configurations. A Graph Convolutional Network (GCN) is trained on these Pareto-optimal graphs to generate strategically effective initial configurations, termed "hot starts". Empirical evaluations demonstrate that the GCN-generated hot starts provide a significant advantage over random configurations. In scenarios considering multiple pursuers and evaders, this method hastens the decline in evader survival rates, reduces pursuer travel distances, and enhances containment, showcasing clear strategic benefits.
Quantal Response-Based Defense in One vs. Many Stackelberg Security Games
Tufts University | 2023-2024
During the 2023-2024 academic year, I researched Stackelberg Security Games and Deception. My work focused on finding reductions from Stackelberg Security Games to social planner problems in macroeconomic models.
Understanding Eviction Rates Induced by COVID: A Statistical, Mechanism Design, and Game Theoretic Perspective
Lafayette College | 2021
During the 2021 academic year, I researched the impacts of COVID-19 on the rental market in Pennsylvania and Easton. I approached this through statistics, econometrics, mechanism design, and game theory. I worked with the city of Easton to help implement these Mechanism Design solutions.