Shanghang Zhang is a Tenure Track Assistant Professor at the School of Computer Science, Peking University. She received her Ph.D. from Carnegie Mellon University in 2018 and conducted postdoctoral research at the University of California, Berkeley. Her research focuses on multimodal foundation models for physical intelligence. As first or corresponding author, she has published over 150 papers in leading journals and conferences, including Nature Methods, Nature Biomedical Engineering, and Nature Computational Science. Her work has received over 36,000 citations on Google Scholar. She received the Best Paper Award at AAAI 2021 and a Best Paper Award nomination at ICRA 2026 (Robot Manipulation and Locomotion). Her book, Deep Reinforcement Learning, published by Springer Nature, was selected for the publisher’s annual collection of high-impact research by Chinese authors and has recorded more than 300,000 electronic downloads worldwide.
She holds over ten patents in the United States and China. She has won the first place in the ICCV Continual Learning Challenge, the Best Interaction Award at the first World Humanoid Robot Games, the champion in the Fully Autonomous Table Tennis Robot Competition at the second World Humanoid Robot Games. She has been recognized as an EECS Rising Star in the United States, and is a member of the Ministry of Industry and Information Technology’s Humanoid Robot Standardization Technical Committee and the China Association for Science and Technology’s Youth 100 group. She has also been named an AI100 Young Pioneer by MIT Technology Review, and included in the World’s Top 2% Scientists list. Her honors include the Shi Qingyun Woman Scientist Award; Japan’s Okawa Foundation Research Grant; the Ant InTech Technology Award—Future; the Ministry of Education–Huawei Intelligent Computing Foundation Award; ByteDance’s Best Advisor Award. She ranked the first among Peking University scholars in CSRankings, a global ranking of computer science research, for two consecutive academic years, 2024–2025 and 2025–2026.
