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Michael’s research is in multi-agent systems and swarm robotics, focusing on scalability and provable safety guarantees. He is particularly interested in how interactions between an increasing number of independent systems can scalably improve decision making or make it safer. Michael is also interested in alignment.
Publications
- When Is Diversity Rewarded in Cooperative Multi-Agent Learning? ICLR 2026 2026
- Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding ICLR 2026 2026
- Remotely Detectable Robot Policy Watermarking ICLR 2026 2026
- Graph Attention-Guided Search for Dense Multi-Agent Pathfinding AAAI 2026 2026
- ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination CoRL 2025 2025