Haimin Hu is an assistant research professor of computer science and a member of the Johns Hopkins Data Science and AI Institute, the Institute for Assured Autonomy, and the Laboratory for Computational Sensing and Robotics.
His research focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic game theory with machine learning and safety-critical control, his work enables deployable, verifiable, and trustworthy systems, from autonomous vehicles to drones and legged robots.
Specifically, his research covers the following topics:
- Uncertainty-aware motion planning: How can robots plan safe and efficient motion by accounting for their evolving uncertainty and their ability to reduce it through future interaction, sensing, communication, and learning?
- Human-AI co-evolution and co-adaptation: How can embodied AI systems learn from human teammates while helping them refine existing skills and acquire new ones in a safe, personalized manner?
- Safe human-compatible safe autonomy: How can autonomous systems ensure prescribed safety while remaining aligned with human values and attuned to human cognitive limitations?
- Scalable and generalizable strategic decision-making: How can multi-robot systems make safe, coordinated decisions in dynamic, human-populated environments?
Hu has been recognized as a 2025 Robotics: Science and Systems Pioneer, a 2025 Cyber-Physical Systems Rising Star, and a 2024 Human-Robot Interaction Pioneer. Additionally, he has served as an associate editor for IEEE Robotics and Automation Letters since his fourth year as a PhD student.
Hu obtained a PhD in electrical and computer engineering from Princeton University in 2025, an MSE in electrical engineering from the University of Pennsylvania in 2020, and a BE in electronic and information engineering from ShanghaiTech University in 2018. He is currently conducting his postdoctoral research at the University of Pennsylvania before joining Johns Hopkins full-time.