Biography

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My research spans machine learning theories and algorithms, with special interests in transfer learning, deep learning, and scientific learning. My research is persistently dedicated to enabling adaptive machine learning in open, dynamic and non-stationary world, including representation learning, predictive learning, transfer learning, 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 enable systematic generalization in data representation, inference, and decision making. We focus on industrial applications of AI and data science, including text, sequence, image, video, and scientific data analysis and understanding.

My Quote

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

Research

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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

Selected Publications [Publications] [DBLP] [Google Scholar]

(* Corresponding Author)

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Thesis

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

Preprints

1. On Localized Discrepancy for Domain Adaptation
   Yuchen Zhang, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv 2020]

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. Transferable Calibration with Lower Bias and Variance in Domain Adaptation
   Ximei Wang, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
   Neural Information Processing Systems (NeurIPS), 2020 (Accepted) [arXiv]

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

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

4. 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]

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]

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]

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]

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]

Libraries

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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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• Rising Star in Science and Technology, Beijing, 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

• Neural Information Processing Systems (NeurIPS) 2020
• International Conference on Learning Representations (ICLR) 2020, 2021

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
• 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, 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

Research Grants

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• Principal Investigator, Industrial Big Data Transfer Learning: Theories and Algorithms, Beijing Nova Program, 2021-2023
• 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 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 -> 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): ->

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 -> Alimama
• 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 ->
• 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 (IIIS)
• Ziping Sun (2016-2020): -> THU Master
• Tianle Liu (2016-2020): -> Harvard PhD
• 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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