Research Interests: Machine learning, Computer vision, Brain-inspired learning
Office Phone: +86 18932862953
Dr. Shanghang Zhang is a Tenure Track Assistant Professor at the School of Computer Science, Peking University. She has been the postdoc research fellow at Berkeley AI Research Lab (BAIR), EECS, UC Berkeley. Her research focuses on OOD Generalization that enables the machine learning systems to generalize to new domains, categories, and modalities using limited labels, with applications to autonomous driving and robotics, as reflected in her over 40 papers on top-tier journals and conference proceedings (Google Scholar Citations: 3100, H-index: 23, I10-index: 35). She has also been the author and editor of the book “Deep Reinforcement Learning: Fundamentals, Research and Applications” published by Springer Nature. This book is selected to Annual High-Impact Publications in Computer Science by Chinese researchers and its Electronic Edition has been downloaded 120,000 times worldwide. Her recent work “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting” has received the AAAI 2021 Best Paper Award. It ranks the 1st place of Trending Research on PaperWithCode and its Github receives 2,600+ Stars. Shanghang has been selected to “2018 Rising Stars in EECS, USA”. She has also been awarded the Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship. Her research outcomes have been successfully productized into real-world machine learning solutions and filed 5 patents. Dr. Zhang has been the chief organizer of several workshops on ICML/NeurIPS, and the special issue on ICMR. Dr. Zhang received her Ph.D. from Carnegie Mellon University in 2018, and her Master from Peking University.