Biography

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My research spans theories, algorithms, and applications of machine learning, with persistent dedication to enable adaptive machine learning in the open, dynamic and non-stationary world. My current research is to make advances in transfer and multi-task learning, sample-efficient learning, spacetime deep learning, and learning with scientific knowledge.

Our Machine Learning Group is interested in developing machine learning technology for systematic generalization in representation, inference, and decision making with good trade-off between accuracy, efficiency, and stability. We focus on industrial applications of AI and data science, including analysis and understanding of sequence, spacetime, and scientific data.

My Quote

“Everything should be made as simple as possible, but no simpler.” --Albert Einstein

Research Interests

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• Machine Learning: Transfer Learning, Deep Learning, Scientific 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

Recent News

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• 2021.05: Four papers got accepted by ICML 2021, congrats! (more)
• 2021.03: Five papers got accepted by CVPR 2021, congrats! (more)
• 2020.09: Four papers got accepted by NeurIPS 2020, congrats! (more)

Selected Publications [Full List] [DBLP] [Google Scholar]

(* Corresponding Author)

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Thesis

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

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. 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]

Conference Proceedings

1. LogME: Practical Assessment of Pre-trained Models for Transfer Learning
   Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long*
   International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix]

2. Representation Subspace Distance for Domain Adaptation Regression
   Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long*
   International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix]

3. 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]

4. Conditional Adversarial Domain Adaptation
   Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
   Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code] (500+ Citations)
   PaperDigest Most Influential Papers

5. 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]

6. 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]

7. 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] (1000+ Citations)
   PaperDigest Most Influential Papers

8. 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] (800+ Citations)

9. 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] (2000+ Citations)
   PaperDigest Most Influential Papers

Invited Talks

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• Transfer Learning: Theories and Algorithms, Chinese Conference on Data Mining, CCDM 2020 [PDF]
• 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]
• Predictive Learning for Video Prediction, Chinese Conference on Data Mining, CCDM 2018 [PDF]
• Deep Hashing for Multimedia Retrieval, International Conference on Data Science, 2017 [PDF]

Software

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• Transfer-Learn: An Open-Source, Highly-Standardized, and Well-Documented Package for Deep Transfer Learning [Code]
• DeepHash: An Open-Source Package for Deep Learning to Hash [Code]

Awards

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• Excellent Young Scholar, National Natural Science Foundation, China, 2020
• Rising Star in Science and Technology, Beijing, 2020
• Popular Dissertation Top 20, CNKI, China, 2020
• First Prize, Technical Invention Award, Ministry of Education (MOE), China, 2018
• Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI), 2016
• Distinguished Dissertation Nominee, China Computer Federation (CCF), 2014
• Outstanding PhD Graduate, Tsinghua University, 2014
• Distinguished Dissertation Award, Tsinghua University, 2014
• Best Paper Nominee, SIAM SDM, 2012

Professional Services

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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 Machine Learning (ICML) 2021
• Neural Information Processing Systems (NeurIPS) 2020, 2021
• International Conference on Learning Representations (ICLR) 2020, 2021
• International Joint Conference on Artificial Intelligence (IJCAI) 2021

Guest Editor

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020, 2021

Senior PC Member

• AAAI Conference on Artificial Intelligence 2020, 2021
• 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
• IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2019, 2020, 2021
• 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, 2020
• 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, 2020
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016, 2017, 2018, 2019, 2020
• 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
• ACM Computing Surveys 2019, 2020
• Nature Communications 2019, 2020
• Nature Human Behavior 2019, 2020

Research Grants

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• Principal Investigator, Transfer Learning, National Natural Science Foundation for Excellent Young Scholars, 2021-2023
• Principal Investigator, Industrial Big Data Transfer Learning: Theories and Algorithms, Beijing Nova Program, 2021-2023
• Principal Investigator, Adaptive Perception and Intelligent Scheduling for Manufacturing Tasks, National Major Program for New Generation AI, 2020-2023
• 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, Recommendation Algorithm based on Multi-Task Learning, Alibaba Innovative Research (AIR), 2020-2021
• 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- (清华大学年度教学优秀奖)
• Instructor: Machine Learning, 2019-
• Instructor: Introduction to Artificial Intelligence, 2021-
• Instructor: Introduction to Data Science, 2018-2020
• Co-Instructor: Introduction to Big Data Technology, 2017-
• Co-Instructor: Foundations of Big Data Systems (A), 2016-
• Co-Instructor: Foundations of Big Data Systems (B), 2015-

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, 清华大学优秀博士学位论文, 清华大学优秀博士毕业生 -> Researcher, MSRA
• Yunbo Wang (2015-2020): Co-supervised with Prof. Philip S. Yu. 麻省理工学院访问学者, 清华大学优秀博士学位论文, 中国计算机学会优秀博士学位论文奖 -> Assistant Professor, Shanghai Jiao Tong University
• 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): ->
• Yuchen Zhang (2020-2024): Co-supervised with Prof. Jianmin Wang ->
• Kaichao You (2020-2025): ->

Masters

• Han Zhu (2014-2017): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文, 清华大学优秀硕士毕业生 -> Alimama
• 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, 清华大学优秀硕士毕业生 -> Alimama
• Jianjin Zhang (2016-2019): -> Microsoft
• Zhifeng Gao (2016-2019): -> Alimama
• Yuchen Zhang (2017-2020): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文 -> THU PhD
• Jintao Liu (2017-2020): Co-supervised with Prof. Jianmin Wang -> ByteDance
• Liang Li (2017-2020): Co-supervised with Prof. Jianmin Wang -> Microsoft
• Sinan Wang (2017-2020): 清华大学优秀硕士学位论文, 清华大学优秀硕士毕业生 -> Alibaba
• Ying Jin (2018-2021): Co-supervised with Prof. Jianmin Wang -> PhD @ MMLab, CUHK
• Xingqiang Du (2018-2021): Co-supervised with Prof. Jianmin Wang -> Kuaishou
• Bo Fu (2018-2021): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文, 清华大学优秀硕士毕业生 -> WeChat
• Bin Liu (2018-2021): 北京市优秀毕业生 -> Alimama
• Chao Huang (2019-2022): ->
• Zhi Kou (2019-2022): ->
• Jincheng Zhong (2019-2022): ->
• Jinghan Gao (2019-2022): ->
• Junguang Jiang (2020-2023): ->
• Haixu Wu (2020-2023): ->
• Jiehui Xu (2020-2023): ->

Undergraduates

• Zhangjie Cao (2014-2018): 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, 清华大学优秀毕业生 -> THU PhD
• Haixu Wu (2016-2020): 清华大学优良毕业生 -> THU Master
• Junguang Jiang (2016-2020): -> THU Master
• Weirui Ye (2016-2020): -> THU PhD
• Ziping Sun (2016-2020): -> THU Master
• Tianle Liu (2016-2020): 清华大学优良毕业生 -> Harvard PhD
• Hong Liu (2017-2021): 清华大学特等奖学金 -> Stanford PhD
• Yong Liu (2017-2021): 清华大学蒋南翔奖学金 -> THU PhD
• Yifei Ji (2017-2021): -> THU Master
• Shijie Wang (2017-2021): -> Brown PhD
• Haoyu Niu (2017-2021): ->CMU Master
• Jialong Wu (2018-2022): ->
• Lanxiang Xing (2018-2022): ->
• Baixu Chen (2018-2022): ->
• Haoyu Ma (2018-2022): ->
• Qi Li (2018-2022): ->

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