Zhe Fu

Zhe Fu is a Stanford Energy Fellow, co-hosted by ASL and DASH lab at Stanford University. Her research lies at the intersection of machine learning, control, and physical systems, with a focus on developing physics-informed and data-driven methods for learning and controlling complex dynamical systems. Her work aims to combine physical principles, machine learning, and AI agents to enable more efficient, safe, and autonomous systems, as well as to accelerate scientific discovery.

Prior to joining Stanford, Zhe earned her Ph.D. in Transportation Engineering and M.S. in Electrical Engineering and Computer Sciences from UC Berkeley, where she was advised by Prof. Alexandre Bayen. Her doctoral research focused on learning and control for mixed-autonomy transportation systems, including large-scale field experiments demonstrating how a small fraction of automated vehicles can improve traffic flow and energy efficiency. She also worked as a research intern at Honda Research Institute in 2023.

Beyond research, Zhe is committed to mentoring the next generation of researchers and building communities for early-career scholars. She has mentored students from diverse backgrounds, with her mentorship recognized by both the Outstanding Graduate Student Peer Mentor Award and the Evergreen Award for Undergraduate Researcher Mentoring. She also serves as the Junior Researcher Engagement Chair of RERITE (Reproducibility in Transportation Engineering), where she leads initiatives to engage early-career researchers and promote reproducible and open research.

Outside the lab, Zhe is an enthusiastic foodie who enjoys exploring restaurants and experimenting in the kitchen. She can also often be found playing tennis and table tennis, skiing, or enjoying live music and performances.

Awards:

• Stanford Energy Postdoctoral Fellowship, 2026
• Rising Star in Data Science, 2026
• First Place, H2H8 YouTube Research Presentation Competition, 2026
• Outstanding PhD Student of the Year, ITS Berkeley, 2026
• Rising Star in EECS, 2025
• Runner-up Winner, Berkeley Grad Slam, 2025
• Rising Star in Cyber-Physical Systems, 2025
• Rising Star in Mechanical Engineering, 2024
• First Place, INFORMS Research Poster Competition, 2023