Reminder to prospective applicants: Opening positions (Postdocs, PhDs and Masters) are available for class 2021. [Instructions]

Biography

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My research spans machine learning theories and algorithms, with special interests in transfer learning, representation learning, and deep learning. In particular, my research is persistently dedicated to advancing major areas of transfer learning, including learning transferable, predictive, and compact representations, inductive transfer, domain adaptation, multitask learning, and learning to learn.

I am leading the Machine Learning Group. Our team is interested in developing foundational theories, effective algorithms, and reliable systems of machine learning to make sense of Out-of-Distribution (OOD) data in representation, inference, and decision making. We focus on real applications of AI and data science, including text, sequence, image & video understanding and scientific data analysis.

Research

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• Machine Learning: Transfer Learning, Representation Learning, Deep Learning

Education

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• PhD in Computer Science, 2008-2014
  Department of Computer Science and Technology, Tsinghua University, Beijing, China
  Advisor: Prof. Jianmin Wang

• Bachelor in Electrical Engineering, 2004-2008
  Department of Electrical Engineering, Tsinghua University, Beijing, China

Research Experience

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• Visiting Researcher, 2014-2015
  Department of Computer Science, UC Berkeley
  Advisor: Prof. Michael I. Jordan

• Postdoc Researcher, 2014-2016
  School of Software, Tsinghua University, Beijing, China
  Advisor: Prof. Jianmin Wang

Publications [DBLP] [Google Scholar] [Semantic Scholar]

(* Corresponding Author)

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Thesis

1. Mingsheng Long. Transfer Learning: Problems and Methods. 1-127, 2014 [PDF] (In Chinese)
   

Preprints

1. Towards Understanding the Transferability of Deep Representations
   Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv]

2. How Does Learning Rate Decay Help Modern Neural Networks?
   Kaichao You, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv]

3. Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation
   Ying Jin, Ximei Wang, Mingsheng Long*, Jianmin Wang [arXiv]

4. Adversarial Pyramid Network for Video Domain Generalization
   Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long*, Jianmin Wang [arXiv]

Journal Articles

1. Transferable Representation Learning with Deep Adaptation Networks
   Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(12):3071-3085, 2019 [Link]

2. Deep Learning of Transferable Representation for Scalable Domain Adaptation
   Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, Philip S. Yu
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(8):2027-2040, 2016 [Link] [Code]

3. Domain Invariant Transfer Kernel Learning
   Mingsheng Long, Jianmin Wang, Jiaguang Sun, Philip S. Yu
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(6):1519-1532, 2015 [Link] [Code]

4. Transfer Learning with Graph Co-Regularization
   Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(7):1805-1818, 2014 [Link] [Code]

5. Adaptation Regularization: A General Framework for Transfer Learning
   Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(5):1076-1089, 2014 [Link] [Code] (ESI Highly Cited Paper)

Conference Proceedings

1. Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification
   Liang Li, Weirui Ye, Mingsheng Long*, Yateng Tang, Jin Xu, Jianmin Wang
   AAAI Conference on Artificial Intelligence (AAAI), 2020 (Accepted)

2. Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
   Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long*, Jianmin Wang
   Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]

3. Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
   Ximei Wang, Ying Jin, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
   Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]

4. Bridging Theory and Algorithm for Domain Adaptation
   Yuchen Zhang, Tianle Liu, Mingsheng Long*, Michael I. Jordan
   International Conference on Machine Learning (ICML), 2019 [PDF] [Appendix] [Code]

5. Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
   Kaichao You, Ximei Wang, Mingsheng Long*, Michael I. Jordan
   International Conference on Machine Learning (ICML), 2019 [PDF] [Code]

6. Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
   Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
   International Conference on Machine Learning (ICML), 2019 [PDF] [Code]

7. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
   Xinyang Chen, Sinan Wang, Mingsheng Long*, Jianmin Wang
   International Conference on Machine Learning (ICML), 2019 [PDF] [Code]

8. Maximum-Margin Hamming Hashing
   Rong Kang, Yue Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
   IEEE International Conference on Computer Vision (ICCV), 2019 [PDF] [Code]

9. Z-Order Recurrent Neural Networks for Video Prediction
   Jianjin Zhang, Yunbo Wang, Mingsheng Long*, Jianmin Wang, Philip S. Yu
   IEEE International Conference on Multimedia and Expo (ICME), 2019

10. Universal Domain Adaptation
    Kaichao You, Mingsheng Long*, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

11. Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
    Hong Liu, Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

12. Learning to Transfer Examples for Partial Domain Adaptation
    Zhangjie Cao, Kaichao You, Mingsheng Long*, Jianmin Wang, Qiang Yang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

13. Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
    Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

14. Eidetic 3D LSTM: A Model for Video Prediction and Beyond
    Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei
    International Conference on Learning Representations (ICLR), 2019 [PDF]

15. Transferable Curriculum for Weakly-Supervised Domain Adaptation
    Yang Shu, Zhangjie Cao, Mingsheng Long*, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF] [Code]

16. Transferable Attention for Domain Adaptation
    Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long*, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF]

17. Conditional Adversarial Domain Adaptation
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code]

18. Generalized Zero-Shot Learning with Deep Calibration Network
    Shichen Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Code]

19. Partial Adversarial Domain Adaptation
    Zhangjie Cao, Lijia Ma, Mingsheng Long*, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2018 [PDF] [Code]

20. Cross-Modal Hamming Hashing
    Yue Cao, Bin Liu, Mingsheng Long*, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2018 [PDF]

21. Deep Triplet Quantization
    Bin Liu, Yue Cao, Mingsheng Long*, Jianmin Wang, Jingdong Wang
    ACM International Conference on Multimedia (ACMMM), 2018 [PDF] [Code]

22. Deep Priority Hashing
    Zhangjie Cao, Ziping Sun, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    ACM International Conference on Multimedia (ACMMM), 2018 [PDF] [Code]

23. PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
    Yunbo Wang, Zhifeng Gao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    International Conference on Machine Learning (ICML), 2018 [PDF] [Code]

24. PredCNN: Predictive Learning with Cascade Convolutions
    Ziru Xu, Yunbo Wang, Mingsheng Long*, Jianmin Wang
    International Joint Conference on Artificial Intelligence (IJCAI), 2018 [PDF] [Code]

25. Partial Transfer Learning with Selective Adversarial Networks
    Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Slides] [Code]

26. HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN
    Yue Cao, Bin Liu, Mingsheng Long*, Jianmin Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF]

27. Deep Cauchy Hashing for Hamming Space Retrieval
    Yue Cao, Mingsheng Long*, Bin Liu, Jianmin Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Code]

28. Multi-Adversarial Domain Adaptation
    Zhongyi Pei, Zhangjie Cao, Mingsheng Long*, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]

29. Unsupervised Domain Adaptation with Distribution Matching Machines
    Yue Cao, Mingsheng Long*, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF]

30. Transfer Adversarial Hashing for Hamming Space Retrieval
    Zhangjie Cao, Mingsheng Long*, Chao Huang, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]

31. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
    Yunbo Wang, Mingsheng Long*, Jianmin Wang, Zhifeng Gao, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF]

32. Learning Multiple Tasks with Multilinear Relationship Networks
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF] [Poster] [Code]

33. HashNet: Deep Learning to Hash by Continuation
    Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    IEEE International Conference on Computer Vision (ICCV), 2017 [PDF] [Code]

34. Correlation Hashing Network for Efficient Cross-Modal Retrieval
    Yue Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    British Machine Vision Conference (BMVC), 2017 [PDF]

35. Deep Transfer Learning with Joint Adaptation Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2017 [PDF] [Slides] [Poster] [Code]

36. Spatiotemporal Pyramid Network for Video Action Recognition
    Yunbo Wang, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Slides] [Code]

37. Deep Visual-Semantic Quantization for Efficient Image Retrieval
    Yue Cao, Mingsheng Long*, Jianmin Wang, Shichen Liu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Code]

38. Transitive Hashing Network for Heterogeneous Multimedia Retrieval
    Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang
    AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF]

39. Collective Deep Quantization for Efficient Cross-Modal Retrieval
    Yue Cao, Mingsheng Long*, Jianmin Wang, Shichen Liu
    AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF] [Code]

40. Unsupervised Domain Adaptation with Residual Transfer Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2016 [PDF] [Poster] [Code]

41. Deep Visual-Semantic Hashing for Cross-Modal Retrieval
    Yue Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang, Philip S. Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016 [PDF]

42. Composite Correlation Quantization for Efficient Multimodal Retrieval
    Mingsheng Long, Yue Cao, Jianmin Wang, Philip S. Yu
    ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016 [PDF] [Slides] [Code]

43. Correlation Autoencoder Hashing for Supervised Cross-Modal Search
    Yue Cao, Mingsheng Long*, Jianmin Wang, Han Zhu
    ACM International Conference on Multimedia Retrieval (ICMR), 2016

44. Deep Quantization Network for Efficient Image Retrieval
    Yue Cao, Mingsheng Long*, Jianmin Wang, Han Zhu, Qingfu Wen
    AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Slides] [Code]

45. Deep Hashing Network for Efficient Similarity Retrieval
    Han Zhu, Mingsheng Long*, Jianmin Wang, Yue Cao
    AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Code]

46. Learning Transferable Features with Deep Adaptation Networks
    Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2015 [PDF] [Slides] [Poster] [Code] (1000+ Citations)

47. Transfer Joint Matching for Unsupervised Domain Adaptation
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 [PDF] [Code]

48. Inherent Replica Inconsistency in Cassandra
    Xiangdong Huang, Jianmin Wang, Jian Bai, Guiguang Ding, Mingsheng Long
    International Congress on Big Data (BigData), 2014
    

49. Local Hybrid Coding for Image Classification
    Wu Xiang, Jianmin Wang, Mingsheng Long
    International Conference on Pattern Recognition (ICPR), 2014
    

50. Transfer Feature Learning with Joint Distribution Adaptation
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
    IEEE International Conference on Computer Vision (ICCV), 2013 [PDF] [Code]

51. Transfer Sparse Coding for Robust Image Representation
    Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 [PDF] [Code]

52. Transfer Learning with Graph Co-Regularization
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
    AAAI Conference on Artificial Intelligence (AAAI), 2012 [PDF] [Code]

53. Topic Correlation Analysis for Cross-Domain Text Classification
    Lianghao Li, Xiaoming Jin, Mingsheng Long
    AAAI Conference on Artificial Intelligence (AAAI), 2012 [PDF] [Code]

54. Dual Transfer Learning
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang
    SIAM International Conference on Data Mining (SDM), 2012 [PDF] (Nominee of Best Paper Award)

55. Transfer Learning via Cluster Correspondence Inference
    Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, Dou Shen
    IEEE International Conference on Data Mining (ICDM), 2010 (Short Paper)

Open Libraries

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• Xlearn: An Open-Source Package for Deep Transfer Learning [Code]
• DeepHash: An Open-Source Package for Deep Learning to Hash [Code]

Honors and Awards

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• First Prize, Technical Invention Award, Ministry of Education (MOE), 2018
• Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI), 2016
• Distinguished Dissertation Award Nominee, China Computer Federation (CCF), 2014
• Tsinghua University Outstanding Ph.D. Graduate, 2014
• Beijing Municipal Government Award for Outstanding Ph.D. Graduates, 2014
• Academic Rising Star, School of Software, Tsinghua University, 2014
• Zhong Shi-Mo Outstanding Graduate Scholarship, Tsinghua University, 2013
• Intel Fellowship, Tsinghua University, 2012
• Nominee of Best Paper Award, SIAM SDM, 2012
• Outstanding Contribution Award, Big Data Group, Tsinghua University, 2012
• Student Travel Award, IEEE ICDM, 2010

Professional Activities

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Invited Talks

• Transfer Learning: Theories and Algorithms, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 [PDF]
• Transfer Learning: Theories and Algorithms, CCF Conference on Artificial Intelligence, CCFAI 2019 [PDF]
• Transfer Learning: From Algorithms to Theories and Back, Vision And Learning SEminar, VALSE 2019 [PDF]
• Transfer Learning: Generalizing Deep Learning across Domains and Tasks, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 [PDF]
• Predictive Learning for Video Prediction, Chinese Conference on Data Mining, CCDM 2018 [PDF]
• Deep Transfer Learning, Google AI China Center Machine Learning Day, 2018 [PDF]
• Recent Advances in Transfer Learning, Samsung Machine Learning Workshop, 2017 [PDF]
• Deep Hashing for Multimedia Retrieval, International Conference on Data Science, 2017 [PDF]

PC Chair

• Transfer and Multi-Task Learning Workshop, NeurIPS, 2015
• Transferring and Adapting Source Knowledge in Computer Vision Workshop and Challenge (TASK-CV), ICCV, 2017

Area Chair

• International Conference on Learning Representations (ICLR) 2020
• IEEE International Conference on Multimedia and Expo (ICME) 2020

Senior PC Member

• AAAI Conference on Artificial Intelligence (AAAI) 2020
• International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2020

PC Member | Reviewer

• International Conference on Machine Learning (ICML) 2014, 2017, 2018, 2019, 2020
• Neural Information Processing Systems (NeurIPS) 2016, 2017, 2018, 2019, 2020
• IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2019, 2020
• IEEE International Conference on Computer Vision (ICCV) 2019
• European Conference on Computer Vision (ECCV) 2018, 2020
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018, 2019
• ACM Multimedia Conference (ACMMM) 2017, 2018
• AAAI Conference on Artificial Intelligence (AAAI) 2018, 2019
• International Joint Conference on Artificial Intelligence (IJCAI) 2017, 2018
• Artificial Intelligence Journal (AIJ) 2016, 2018
• Journal of Machine Learning Research (JMLR) 2018
• International Journal of Computer Vision (IJCV) 2019
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016, 2017, 2018, 2019
• IEEE Transactions on Knowledge and Data Engineering (TKDE) 2014, 2015, 2016, 2017, 2018, 2019
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2016, 2017, 2018, 2019
• IEEE Transactions on Image Processing (TIP) 2014, 2015, 2016, 2017, 2018, 2019
• IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2018
• IEEE Transactions on Multimedia (TMM) 2018
• IEEE Transactions on Cybernetics (TC) 2018
• ACM Computing Surveys 2019
• ACM Transactions on Knowedge Discovery from Data (TKDD) 2016
• ACM Transactions on Intelligent Systems and Technology (TIST) 2017
• ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) 2014, 2015
• Nature Communications 2019

Research Grants

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• Principal Investigator, Robust Transfer Learning for OOD Data in Open Dynamic Environment, MOE Strategic Research Project on Artificial Intelligence Algorithms (Project PI: Prof. Yang Yu @ Nanjing University), 2020-2021
• Principal Investigator, Transfer Learning Algorithms, Kuaishou Research, 2019-2020
• Principal Investigator, Multimodal Learning Algorithms, Kuaishou Research, 2019-2020
• Principal Investigator, Recommendation Algorithm based on Deep Learning, Alibaba Innovative Research (AIR), 2018-2019
• Principal Investigator, Intelligent Severe Weather Nowcasting System, China Meteorological Administration, 2019-2020
• Principal Investigator, Spatiotemporal Deep Learning for Severe Weather Nowcasting, National Key R&D Plan, 2018-2022
• Principal Investigator, Spatiotemporal Deep Learning: Algorithms and Applications, Natural Science Foundation of China, 2018-2021
• Principal Investigator, Safe Transfer Learning for Big Data Analytics, Natural Science Foundation of China, 2016-2018
• Principal Investigator, Scalable Transfer Learning: Theory and Algorithms, China Postdoc Science Foundation, 2015-2016

Teaching

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• Instructor: Deep Learning, 2018-Present (清华大学年度教学优秀奖, 2019)
• Instructor: Machine Learning, 2019-Present
• Instructor: Introduction to Data Science, 2018-Present
• Co-Instructor: Introduction to Big Data Technology, 2017-Present
• Co-Instructor: Foundations of Big Data Systems (B), 2015-Present
• Co-Instructor: Foundations of Big Data Systems (A), 2016-Present

Students

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PhDs

• Yue Cao (2014-2019): Co-supervised with Prof. Jianmin Wang. 清华大学特等奖学金获得者, 清华大学优秀博士学位论文 -> MSRA
• Yunbo Wang (2015-2020): Co-supervised with Prof. Philip S. Yu. 麻省理工学院 (MIT) 访问学者 ->
• Zhongyi Pei (2015-2020): Co-supervised with Prof. Philip S. Yu -> THU Postdoc
• Ximei Wang (2017-2022): Co-supervised with Prof. Jianmin Wang ->
• Xinyang Chen (2017-2022): ->
• Yang Shu (2018-2023): ->
• Zhiyu Yao (2019-2024): ->

Masters

• Wu Xiang (2011-2014): Co-supervised with Prof. Jianmin Wang -> Bytedance
• Hao Hu (2011-2014): Co-supervised with Prof. Jianmin Wang -> Google Mountain View
• Wei Li (2012-2015): Co-supervised with Prof. Jianmin Wang -> Google Mountain View
• Yateng Tang (2013-2016): Co-supervised with Prof. Jianmin Wang -> WeChat
• Han Zhu (2014-2017): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文 -> Alimama (A-Star)
• Feng Li (2014-2017): Co-supervised with Prof. Jianmin Wang -> Alimama
• Qiaoan Chen (2014-2017): Co-supervised with Prof. Jianmin Wang -> WeChat
• Qingfu Wen (2015-2018): Co-supervised with Prof. Jianmin Wang -> SenseTime
• Jue Wang (2015-2018): Co-supervised with Prof. Jianmin Wang -> Government
• Ziru Xu (2016-2019): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文 -> Alimama
• Jianjin Zhang (2016-2019): -> Microsoft
• Zhifeng Gao (2016-2019): -> Alimama
• Yuchen Zhang (2017-2020): Co-supervised with Prof. Jianmin Wang ->
• Jintao Liu (2017-2020): Co-supervised with Prof. Jianmin Wang ->
• Liang Li (2017-2020): Co-supervised with Prof. Jianmin Wang ->
• Sinan Wang (2017-2020): ->
• Ying Jin (2018-2021): Co-supervised with Prof. Jianmin Wang ->
• Xingqiang Du (2018-2021): Co-supervised with Prof. Jianmin Wang ->
• Bo Fu (2018-2021): Co-supervised with Prof. Jianmin Wang ->
• Bin Liu (2018-2021): ->
• Chao Huang (2019-2022): ->
• Zhi Kou (2019-2022): ->
• Jincheng Zhong (2019-2022): ->
• Jinghan Gao (2019-2022): ->

Undergraduates

• Zhangjie Cao (2014-2018): 清华大学特等奖学金提名, 清华大学优良毕业生 -> Stanford PhD
• Shichen Liu (2014-2018): 清华大学优良毕业生 -> USC PhD
• Bin Liu (2014-2018): 清华大学优良毕业生 -> THU Master
• Lijia Ma (2015-2019): -> UW PhD
• Hongyu Zhu (2015-2019): -> CMU Master
• Zhiyu Yao (2015-2019): -> THU PhD
• Chao Huang (2015-2019): -> THU Master
• Zhi Kou (2015-2019): -> THU Master
• Kaichao You (2016-2020): 清华大学特等奖学金获得者 -> THU PhD
• Weirui Ye (2016-2020): -> THU PhD (Advisor: Andrew Yao @ IIIS)
• Junguang Jiang (2016-2020): -> THU Master
• Haixu Wu (2016-2020): -> THU Master
• Ziping Sun (2016-2020): -> THU Master
• Tianle Liu (2016-2020): ->
• Hong Liu (2017-2021): 清华大学蒋南翔奖学金获得者 ->
• Yong Liu (2017-2021): 清华大学蒋南翔奖学金获得者 ->
• Yifei Ji (2017-2021): ->
• Shijie Wang (2017-2021): ->
• Jialong Wu (2018-2022): ->
• Qi Li (2018-2022): ->

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