Dr. Shanghang Zhang is a Tenure Track Assistant Professor at the Computer Science Department of Peking University. She has been the postdoc research fellow at Berkeley AI Research Lab (BAIR), EECS, UC Berkeley, working with Prof. Kurt Keutzer and Prof. Trevor Darrell. Her research focuses on OOD Generalization that can enable the machine learning systems to generalize to new domains, categories, and modalities using limited labels, with applications to IoT problems including autonomous driving and intelligent manufacture, as reflected in her over 40 papers on top-tier journals and conference proceedings, including NeurIPS, ICLR, ACM MM, TNNLS, TMM, CVPR, ICCV, and AAAI (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. She has been selected to “2018 Rising Stars in EECS, USA”.
Out of Distribution Generalization
Low shot learning
2013 - 2018
Carnegie Mellon University
Advised by Prof. Jose M.F. Moura, and Joao P. Costeira.
2010 - 2013
Advised by Prof. Wen Gao and Xiaodong Xie.
2006 - 2010
AAAI Best Paper Award, 2021
Rising Stars in EECS, US 2018
NIPS travel award; CVPR Doctoral Consortium Travel Award, 2018
Qualcomm Innovation Fellowship (QInF), Finalist Award, 2015
Chiang Chen Overseas Graduate Fellowship, 2013 (10 winners nationwide)
Senior Program Committee, AAAI Conference on Artificial Intelligence (AAAI), 2022 & 2023.
Organizing Chair, Advances in Neural Information Processing Systems (NeurIPS) 2022, 1st Human in the Loop Learning Workshop.
Chief Organizer, International Conference on Machine Learning (ICML) 2021, Self-Supervised Learning for Reasoning and Perception.
Chief Organizer, International Conference on Machine Learning (ICML) 2021, 3rd Human in the Loop Learning Workshop.
Guest Editor, Special Issue on Novel Technologies in Multimedia Big Data, Electronics (ISSN 2079-9292).
Chief Organizer, Conference on Neural Information Processing Systems (NeurIPS) 2020, Self-Supervised Learning-Theory and Practice Workshop.
Chief Organizer, International Conference on Machine Learning (ICML) 2020, 2nd Human in the Loop Learning Workshop.
Chief Organizer, International Conference on Machine Learning (ICML) 2019, 1st Human in the Loop Learning Workshop.
Chief Organizer, ACM International Conference on Multimedia Retrieval (ICMR) 2019, "MMAC: Multi-Modal Affective Computing of Large-Scale Multimedia Data" Special Session.