The lab

Diversity-seeking learning algorithms using reinforcement learning and evolutionary computation for robotic system resilience.

Coordinates lab space, events, resources, and grant administration.

Intersection of mathematics, ML theory, and robotics — parameterizing uncertainty and resilience in multi-agent systems.

Wireless data communications and multi-robot control algorithms for drone swarms and automotive networks.

Moving beyond monolithic AI toward specialized, collaborative robot teams — bridging theory with real-world drone experiments.

Making GNNs safer with emphasis on robustness and interpretability in multi-agent contexts.

MARL representing agent intentions without communication — exploring heterogeneity in coordination.

Moving cutting-edge research to real-world applications across computer vision, imitation learning, and RL.