About

Karen Leung is a robotics and autonomous systems researcher whose work sits at the intersection of safety-critical planning, formal methods, and human-robot interaction. She is perhaps best known for her pioneering research on integrating reachability-based safety assurance into autonomous driving frameworks, with her 2020 paper on the topic accumulating 93 citations — a testament to its significant influence on the field. By developing methods that account for uncertainty in human driver behavior without compromising safety guarantees, Leung has helped bridge the gap between theoretical safety frameworks and real-world deployment challenges. A particularly notable thread in her research involves Signal Temporal Logic (STL), where she introduced STLCG, a computation graph-based technique enabling backpropagation through logical specifications. This innovation, cited over 80 times across multiple publications, allows gradient-based machine learning methods to directly incorporate structured temporal rules — a powerful tool for robot planning and control. Her work on multimodal probabilistic models further addresses the challenge of predicting multiple plausible human behaviors in interactive scenarios, such as highway merging and crowded pedestrian environments. Across her career, Leung has consistently advanced the principled integration of formal logic, safety theory, and learning-based approaches in autonomous robotics.

Research Focus

Key Achievements

8
H-Index
12
Papers
267
Total Citations
22
Avg Citations/Paper
🏆 Most Cited Paper
On infusing reachability-based safety assurance within planning frameworks for human–robot vehicle interactions
93 citations · 2020
📈 Most Prolific Year: 2020 (4 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: Stanford University, City University of Hong Kong, University of Washington, Vaughn College of Aeronautics and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago