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.
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021, February). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of AAAI. AAAI Outstanding Paper Award.
Zhang, S., Wu, G., Costeira, J. P., & Moura, J. M. (2017). Understanding traffic density from large-scale web camera data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5898-5907).
Zhang, S., Wu, G., Costeira, J. P., & Moura, J. M. (2017). Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 3667-3676).
Zhang, S., Shen, X., Lin, Z., Měch, R., Costeira, J. P., & Moura, J. M. (2018). Learning to understand image blur. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 6586-6595).
Zhao, H., Zhang, S., Wu, G., Moura, J. M., Costeira, J. P., & Gordon, G. J. (2018). Adversarial multiple source domain adaptation. Advances in neural information processing systems (NeurIPS), 31.
J. Ni, S. Zhang, H, Xie, “Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning”, Advances in Neural Information Processing Systems (NeurIPS), 2019.
X. Ma, X. Kong, S. Zhang, E. Hovy, “MaCow: Masked Convolutional Generative Flow”, Advances in Neural Information Processing Systems (NeurIPS), 2019.
Zhao, S.#, Wang, G.#, Zhang, S.#, Gu, Y., Li, Y., Song, Z., ... & Keutzer, K. (2020, April). Multi-source distilling domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (Vol. 34, No. 07, pp. 12975-12983).
Zhao, S., Yue, X., Zhang, S., Li, B., Zhao, H., Wu, B., ... & Keutzer, K. (2020). A review of single-source deep unsupervised visual domain adaptation. IEEE Transactions on Neural Networks and Learning Systems (TNNLS, IF 14.255).
Dong, H., Dong, H., Ding, Z., Zhang, S., & Chang. (2020). Deep Reinforcement Learning. Springer Singapore.
X. Sun, Y. Xu, P. Cao, Y. Kong*, L. Hu, S. Zhang*, Y.Wang, “TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning”,European Conference on Computer Vision (ECCV) 2020, Oral presentation.
K. Mei, C. Zhu, J. Zou, S. Zhang, “Instance Adaptive Self-Training for Unsupervised Domain Adaptation”, 16th European Conference on Computer Vision (ECCV), 2020.
Li, B.#, Wang, Y.#, Zhang, S.#, Li, D., Keutzer, K., Darrell, T., & Zhao, H. (2021). Learning invariant representations and risks for semi-supervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1104-1113).
S. Zhou, S. Zhang, et al. “Caching in Dynamic Environments: a Near-optimal Online Learning Approach”, IEEE Transactions on Multimedia (TMM, IF 8.182), 2021.
H. Zhou, J. Li, J. Peng, S. Zhang, S. Zhang, “Triplet Attention: Rethinking the similarity in Transformers”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.
X Ma, X Kong, S Zhang, E Hovy, “Decoupling Global and Local Representations via Invertible Generative Flows”, Accepted by International Conference on Learning Representations (ICLR), 2021.
T. Li, X. Chen, S. Zhang*, Z. Dong*, K. Keutzer, “Cross-Domain Sentiment Classification With Contrastive Learning and Mutual Information Maximization”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
C. Zhang#, M. Zhang#, S. Zhang#, et al. "Delving deep into the generalization of vision transformers under distribution shifts.", Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
M. Liu, Q. Zhou, H. Zhao, L. Du, Y. Du, J. Li, K. Keutzer, S. Zhang*. Prototypical Supervised Contrastive Learning for LiDAR Point Cloud Panoptic Segmentation, International Conference on Robotics and Automation (ICRA), 2022.
S. Zhou, H. Zhao, S. Zhang*, et al. “Online Continual Adaptation with Active Self-Training”, Artificial Intelligence and Statistics Conference (AISTATS), 2022.
S. Zhou, S. Zhang*, et al. “Active Gradual Domain Adaptation: Dataset and Approach”, IEEE Transactions on Multimedia (TMM, IF 8.182), 2022.
J. Yu, J. Liu, X.Wei, H. Zhou, Y. Nakata, D. Gudovskiy, T. Okuno, J. Li, K. Keutzer, S. Zhang*, MTTrans: Cross-Domain Object Detection with Mean Teacher Transformer, 17th European Conference on Computer Vision (ECCV) 2022.
X. Li, J. Liu, S.Wang, C. Lyu, M. Lu, Y. Chen, A. Yao, Y. Guo, S. Zhang*, Efficient Meta-Tuning for Content-aware Neural Video Delivery, 17th European Conference on Computer Vision (ECCV) 2022.
Chu, X., Jin, Y., Zhu, W., Wang, Y., Wang, X., Zhang, S. and Mei, H., 2022, June. DNA: Domain Generalization with Diversified Neural Averaging. In International Conference on Machine Learning (pp. 4010-4034) (ICML). PMLR.
Y Zou, S Zhang, Y Li, R Li, Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation, Neural Information Processing Systems (NeurIPS) 2022.
H Zhou, S Xiao, S Zhang, J Peng, S Zhang, J Li, Jump Self-attention: Capturing High-order Statistics in Transformers, Neural Information Processing Systems (NeurIPS) 2022.
My research focuses on machine learning generalization in the open world, including theory, algorithm, and system development, with applications to important IoT problems such as smart traffic and intelligent manufacture. Especially, by investigating the brain cognition mechanism, I develop generalized and efficient machine learning system that can adapt to new domains and modalities with limited labels.
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.
Member, IEEE, IEEE Women in Engineering, IEEE Computer Society, IEEE Signal Processing Society.
Member, Association for Computing Machinery (ACM), ACM-SIGMM, ACM-SIGAI.
Reviewer, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), International Journal of Computer Vision (IJCV), IEEE Signal Processing Magazine (SPM), Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (TMM), IEEE Signal Processing Letters (SPL), The ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).
Reviewer/Program Committee, NeurIPS; ICLR; CVPR; ICCV; ECML; AAAI; IJCAI.
