Negar Mehr’s research focuses on creating algorithms and mathematical models for autonomous systems to interact safely and intelligently with humans and other autonomous systems. Her research interests lie at the intersection of robotics, control theory, game theory, and machine learning. She has published in top robotics and control journals and conferences. Her research has had applications in various domains including space exploration, autonomous driving, UAVs and assistive robots. She runs the Intelligent Control (ICON) lab where her group utilizes robotic hardware for verifying and testing their developed control algorithms.
Relevant expertise:
- Safe multi-vehicle coordination: developing frameworks for autonomous conflict resolution and maintaining vehicle safety in uncertain real-world scenarios with scalable vehicle configurations
- Guaranteed safety for RL-based controllers: creating systems that ensure autonomous controllers never lead to constraint violations while maintaining safety throughout training and deployment
- Collaborative perception and mapping: aggregating information across multiple agents for building accurate maps and perception of the environment