仉尚航

姓名：仉尚航

职称：研究员、助理教授、博士生导师、博雅青年学者

办公室：理科二号楼 2728室

电话：+86 18932862953

研究方向：开放世界泛化机器学习、类脑视觉感知与学习、AI驱动科学计算

个人主页：https://www.shanghangzhang.com/

电子邮箱：shanghang@pku.edu.cn

通信地址：北京市海淀区颐和园路5号北京大学理科二号楼 2728室

个人简介

仉尚航现为北京大学计算机学院任长聘系列助理教授（研究员），博士生导师。于2018年博士毕业于美国卡内基梅隆大学，后于2020年初加入加州大学伯克利分校 Berkeley AI Research Lab (BAIR) 从事博士后研究。其研究方向为开放环境泛化机器学习理论与系统，同时在计算机视觉和类脑智能方向拥有丰富研究经验。在人工智能顶级期刊和会议上发表论文50余篇，并申请5项美中专利。荣获世界人工智能顶级会议AAAI’2021 最佳论文奖，该工作曾列世界最大学术源代码仓库Trending Research 榜单第一，受到十余家媒体报道推广，开源代码被访问7万余次、2600余次Star。作为编辑和作者由Springer Nature出版英文书籍《Deep Reinforcement Learning》，至今电子版全球下载量超十二万次，入选中国作者年度高影响力研究精选，并出版中文译本。Google Scholar引用数3100次，h-index 23, i10-index 35。于2018年入选美国“EECS Rising Star”，曾获得Adobe学术合作基金，Qualcomm创新奖提名。获国际人脑多模态计算模型响应预测竞赛第一名，NeurIPS 2021 Visual Domain Adaptation 竞赛第三名。曾多次在国际顶级会议NeurIPS、ICML上组织Workshop，多次作为国际顶级期刊和会议的审稿人或程序委员，担任AAAI 2022/2023 高级程序委员。

主要研究领域

机器学习与计算机视觉，视觉信息处理与类脑智能

教育背景

  • 2013.9-2018.5
    卡内基梅隆大学，电子与计算机工程学院，博士
    导师：José Moura, João Costeira

  • 2010.9-2013.7
    北京大学，信息科学技术学院，微电子学专业，硕士
    导师：高文，解晓东

  • 2006.9-2010.6
    东南大学，电子工程学院，电子科学与技术专业，学士

工作经历

  • 2022.1-至今
    北京大学，计算机学院，研究员、助理教授、博导、博雅青年学者

  • 2020.1-2022.1
    加州大学伯克利分校 EECS，Berkeley AI Research Lab，博士后研究员，合作导师：Kurt Keutzer, Trevor Darrell

  • 2018.8-2020.1
    Petuum Inc.美国，研究经理

获奖经历

  • 2021年 世界人工智能顶级会议 AAAI Outstanding Paper Award (2/9034 Submissions)

  • 2020年 国家海外高层次人才青年项目

  • 2018年 美国 EECS Rising Star

  • 2021年 人脑多模态计算模型竞赛冠军（由美国国防部基础研究部和MIT-IBM沃森人工智能实验室联合举办）

  • 2015年 高通创新奖提名

主要科研项目

  1. 自主意识学习，国家自然科学基金委员会专项项目

  2. 面向驾驶场景的高真实感数据合成与视觉模型训练平台

  3. 面向自动驾驶的开放环境泛化机器学习, CCF-滴滴盖亚青年学者科研基金项目

  4. 面向自动驾驶的跨场景泛化3D感知关键技术研究，CCF-百度松果基金项目

主要学术任职

  • 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.

代表论文

  1. 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.

  2. 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).

  3. 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).

  4. 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).

  5. 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.

  6. J. Ni, S. Zhang, H, Xie, “Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning”, Advances in Neural Information Processing Systems (NeurIPS), 2019.

  7. X. Ma, X. Kong, S. Zhang, E. Hovy, “MaCow: Masked Convolutional Generative Flow”, Advances in Neural Information Processing Systems (NeurIPS), 2019.

  8. 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).

  9. 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).

  10. Dong, H., Dong, H., Ding, Z., Zhang, S., & Chang. (2020). Deep Reinforcement Learning. Springer Singapore.

  11. 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.

  12. K. Mei, C. Zhu, J. Zou, S. Zhang, “Instance Adaptive Self-Training for Unsupervised Domain Adaptation”, 16th European Conference on Computer Vision (ECCV), 2020.

  13. 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).

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. S. Zhou, H. Zhao, S. Zhang*, et al. “Online Continual Adaptation with Active Self-Training”, Artificial Intelligence and Statistics Conference (AISTATS), 2022.

  21. S. Zhou, S. Zhang*, et al. “Active Gradual Domain Adaptation: Dataset and Approach”, IEEE Transactions on Multimedia (TMM, IF 8.182), 2022.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. 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.

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