Biography

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My research spans machine learning theory, algorithms and models, with persistent commitment to creating strong learning machines from big data that adapt to the real world. I am working on deep learning and foundation models, scientific learning and world models, transfer learning and model adaptation.

Our Machine Learning Group is interested in powering machine learning for representation, perception, prediction, and generation of big data with a good tradeoff between accuracy, efficiency, generalizability, and transferability. Our mission is to solve open problems and enable major applications of Artificial Intelligence (AI), including physical sciences, hybrid AI systems, and industrial software.

My Quote

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

Research Interests

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• Machine Learning: Deep Learning, Transfer Learning, Scientific Machine Learning
• Artificial Intelligence: Earth Model, Neural Solver, Time Series, Spatiotemporal Dynamics

Education

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

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

Research Experience

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• Postdoc in Machine Learning, 2014-2015
  Department of Computer Science, UC Berkeley
  Advisor: Prof. Michael I. Jordan

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

Highlights

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• Big nowcasting model for extreme precipitation (NowcastNet) was reported in News and Views and published in Nature 2023

• Unified forecasting model for worldwide stations (Corrformer) was published as the Cover Article in Nat Mach Intell 2023

• Base forecasting model for time series (Autoformer) was ranked 10th of the most influential papers in NeurIPS 2021

• Conditional Domain Adversarial Network (CDAN) was ranked 6th of the most influential papers in NeurIPS 2018

• Joint Adaptation Network (JAN) was ranked 10th of the most influential papers in ICML 2017

• Deep Adaptation Network (DAN) was ranked 5th of the most influential papers in ICML 2015, Test of Time Award at FTL-IJCAI

• Joint Distribution Adaptation (JDA) was ranked 2nd of the most influential papers in ICCV 2013

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. Skilful Nowcasting of Extreme Precipitation with NowcastNet
   Yuchen Zhang, Mingsheng Long^✉, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan^✉, Jianmin Wang^✉
   Nature 619, 526–532, 2023 [Link] [Code]
   ⚑ Hot Paper
   ⚑ WAIC Youth Outstanding Paper Award

2. Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model
   Haixu Wu, Hang Zhou, Mingsheng Long^✉, Jianmin Wang^✉
   Nature Machine Intelligence (Nat Mach Intell) 5, 602-611, 2023 [Link] [Code]
   ⚑ Cover Article
   ⚑ WAIC Youth Outstanding Paper Award Honorable Mention

3. One Fits Many: Class Confusion Loss for Versatile Domain Adaptation
   Ying Jin, Zhangjie Cao, Ximei Wang, Jianmin Wang, and Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 [Link] [Code]

4. Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
   Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(12):15275-15291, 2023 [Link] [Code]

5. ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
   Zhiyu Yao, Yunbo Wang, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(11):13281-13296, 2023 [Link] [Code]

6. PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
   Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2):2208-2225, 2023 [Link] [Code]
   ⚑ Highly Cited Paper

7. From Big to Small: Adaptive Learning to Partial-Set Domains
   Zhangjie Cao, Kaichao You, Ziyang Zhang, Jianmin Wang, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2):1766-1780, 2023 [Link] [Code]

8. VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
   Zhiyu Yao, Yunbo Wang, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(11):7989-8004, 2022 [Link] [Code]

9. Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
   Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long^✉
   Journal of Machine Learning Research (JMLR), 23(209):1-47, 2022 [Link] [PDF] [Code]

10. 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] [Code]
    ⚑ Highly Cited Paper

Conference Proceedings

1. RoPINN: Region Optimized Physics-Informed Neural Networks
   Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

2. DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
   Qilong Ma, Haixu Wu, Lanxiang Xing, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

3. iVideoGPT: Interactive VideoGPTs are Scalable World Models
   Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

4. Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting
   Jincheng Zhong, Xingzhuo Guo, Jiaxiang Dong, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

5. TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
   Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

6. AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
   Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

7. Transolver: A Fast Transformer Solver for PDEs on General Geometries
   Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]
   ⚑ Spotlight Paper

8. HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
   Lanxiang Xing, Haixu Wu, Yuezhou Ma, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

9. Timer: Generative Pre-trained Transformers are Large Time Series Models
   Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

10. TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
    Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

11. CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
    Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv]

12. HarmonyDream: Task Harmonization Inside World Models
    Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

13. Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers
    Zhiyu Yao, Jian Wang, Haixu Wu, Jingdong Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [Code]

14. On the Embedding Collapse when Scaling up Recommendation Models
    Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

15. iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
    Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2024 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

16. Efficient ConvBN Blocks for Transfer Learning and Beyond
    Kaichao You, Guo Qin, Anchang Bao, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2024 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

17. Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
    Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

18. ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
    Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

19. Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
    Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

20. SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
    Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

21. Solving High-Dimensional PDEs with Latent Spectral Models
    Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [arXiv] [Code]

22. CLIPood: Generalizing CLIP to Out-of-Distributions
    Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [arXiv] [Code]

23. Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
    Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [Code]

24. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
    Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2023 [PDF] [arXiv] [Code]

25. Debiased Self-Training for Semi-Supervised Learning
    Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Slides] [Code]
    ⚑ Oral Paper

26. Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
    Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv]

27. Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
    Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Code]

28. Supported Policy Optimization for Offline Reinforcement Learning
    Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Code]

29. Flowformer: Linearizing Transformers with Conservation Flows
    Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2022 [PDF] [arXiv] [Code]

30. Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
    Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

31. Decoupled Adaptation for Cross-Domain Object Detection
    Junguang Jiang, Baixu Chen, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]

32. X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
    Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]

33. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
    Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2021 [PDF] [Appendix] [arXiv] [Code]
    ⚑ Ranks 10th in NeurIPS 2021

34. Cycle Self-Training for Domain Adaptation
    Hong Liu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2021 [PDF] [Appendix] [Code]

35. 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] [Code]

36. Zoo-Tuning: Adaptive Transfer from A Zoo of Models
    Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

37. Self-Tuning for Data-Efficient Deep Learning
    Ximei Wang, Jinghan Gao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

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

39. 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 [PDF] [Appendix] [Code]

40. Co-Tuning for Transfer Learning
    Kaichao You, Zhi Kou, Mingsheng Long^✉, Jianmin Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

41. Stochastic Normalization
    Zhi Kou, Kaichao You, Mingsheng Long^✉, Jianmin Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

42. Learning to Adapt to Evolving Domains
    Hong Liu, Mingsheng Long^✉, Jianmin Wang, Yu Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

43. Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
    Zhiyu Yao, Yunbo Wang, Mingsheng Long^✉, Jianmin Wang
    International Conference on Machine Learning (ICML), 2020 [PDF] [Code]

44. 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]

45. 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]

46. 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]
    ⚑ Long Oral Paper

47. 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]
    ⚑ Long Oral Paper

48. 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]

49. 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]

50. Conditional Adversarial Domain Adaptation
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code]
    ⚑ Ranks 6th in NeurIPS 2018

51. 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]

52. 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]

53. 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] [Code]

54. 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]

55. 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]
    ⚑ Ranks 10th in ICML 2017

56. 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]

57. 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]
    ⚑ Ranks 5th in ICML 2015
    ⚑ Test of Time Award at FTL-IJCAI 2021

Software

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• Transfer Learning Library (TLlib): An easy-to-use library for transfer learning [Code @ GitHub]
• Time Series Library (TSlib): An easy-to-use package for time series analysis [Code @ GitHub]

Talks

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• Foundation Models for Time Series Analysis, The Global Artificial Intelligence Technology Conference, GAITC 2023 [PDF]
• Domain Adaptation: Theory, Algorithms, and Open Library, Workshop on Distribution Shifts, NeurIPS 2022 [PDF]
• Transferability in Deep Learning, China Workshop on Machine Learning and Applications, MLA 2022 [PDF]
• Adaptive Transfer Learning from Pre-trained Models, Vision And Learning SEminar, VALSE 2022 [PDF]
• Deep Learning Models for Sequential Data Analysis, Chinese Conference on Data Mining, CCDM 2022 [PDF]
• Transfer Learning: Theories, Algorithms, and Open Library, Workshop on Federated and Transfer Learning, IJCAI 2021 [PDF]
• Transfer Learning: Theories and Algorithms, Chinese Conference on Data Mining, CCDM 2020 [PDF]
• Transfer Learning: Theories and Algorithms, CCF Conference on Artificial Intelligence, CCFAI 2019 [PDF]
• Predictive Learning for Video Prediction, Chinese Conference on Data Mining, CCDM 2018 [PDF]

Selected Awards

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• Outstanding Paper Award, World Artificial Intelligence Conference, 2024
• Excellent Worker, Tsinghua University, 2024
• Excellent Advisor, Tsinghua University, 2023
• Highlighted Area Chair, ICLR 2022
• Test of Time Award, FTL-IJCAI, 2021
• World's Top 2% Scientists (by Career), 2024
• Highly Cited Scholar, Elsevier, 2021-2023
• AI 2000 Most Influential Scholar Award in Machine Learning, 2021-2023
• First Prize, Science and Technology Progress Award, Beijing, 2021
• Early Career Award, NSFC, China, 2020
• Beijing Nova Talent, Beijing, 2020
• First Prize, Technical Invention Award, Ministry of Education (MOE), China, 2018
• Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI), 2016

Professional Services

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Senior Area Chair

• International Conference on Machine Learning (ICML) 2024, 2025
• Neural Information Processing Systems (NeurIPS) 2023, 2024

Area Chair

• International Conference on Machine Learning (ICML) 2021, 2022, 2023
• Neural Information Processing Systems (NeurIPS) 2020, 2021, 2022
• International Conference on Learning Representations (ICLR) 2020, 2021, 2022, 2023, 2024
• International Conference on Computer Vision (ICCV) 2023
• Conference on Computer Vision and Pattern Recognition (CVPR) 2024
• AAAI Conference on Artificial Intelligence (AAAI) 2022, 2023
• International Joint Conference on Artificial Intelligence (IJCAI) 2021

Associate Editor

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2021, 2022, 2023, 2024
• Artificial Intelligence Journal (AIJ) 2023, 2024
• Transactions on Machine Learning Research (TMLR) 2022, 2023, 2024

Guest Editor

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020, 2021, 2022

Program Chair

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

(Senior) 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, 2022
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018, 2019, 2020
• Artificial Intelligence Journal (AIJ) 2016, 2018, 2022
• Journal of Machine Learning Research (JMLR) 2018, 2020, 2021, 2022, 2023
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
• IEEE Transactions on Knowledge and Data Engineering (TKDE) 2014, 2015, 2016, 2017, 2018, 2019, 2020
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2016, 2017, 2018, 2019, 2020
• ACM Computing Surveys 2019, 2020
• Nature Machine Intelligence 2024
• Nature Human Behavior 2019, 2020
• Nature Communications 2019, 2020
• Science Advances, 2023, 2024

Research Grants

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• PI, Large Meteorology Models for Extreme Precipitation Nowcasting, NSFC Key Project, 2024-2027
• PI, Dynamic System Workload Prediction and Optimization, 2024-2025
• PI, World Model for Reinforcement Learning, 2024-2025
• PI, Innovation Center for Time Series and IoT Applications, 2022-2025
• PI, Adaptive Machine Learning based on Brain-Inspired Mechanism, 2022-2023
• PI, Transfer Learning, NSFC Early Career Award Project, 2021-2023
• PI, Adaptive Perception and Intelligent Schedualing for Manufacturing Resources, National Key R&D Program, 2020-2023
• PI, Transfer Learning for Visual Applications, 2018-2021
• PI, Recommendation Algorithm based on Deep Learning, 2018-2021
• PI, Intelligent Weather Forecasting System, China Meteorological Administration, 2017-2022
• PI, Spatiotemporal Deep Learning: Algorithms and Applications, NSFC, 2018-2021
• PI, Safe Transfer Learning for Big Data Analytics, NSFC, 2016-2018

Teaching

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• Instructor: Deep Learning, 2018- 清华大学年度教学优秀奖, 清华大学精品课
• Instructor: Machine Learning, 2019- 清华大学年度教学优秀奖, 清华大学精品课, 毕业生心目中的好教师及课程
• Instructor: Introduction to Artificial Intelligence, 2021- 清华大学年度教学优秀奖, 清华大学精品课, 毕业生心目中的好教师及课程
• Instructor: Introduction to Data Science, 2018-2020
• Instructor: Big Data Technology and Applications, 2019
• Co-Instructor: Software Engineering: Exploration and Practice, 2023-2024
• Co-Instructor: Introduction to Big Data Technology, 2017-2020
• Co-Instructor: Foundations of Big Data Systems (A), 2016-2020
• Co-Instructor: Foundations of Big Data Systems (B), 2015-2020

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 -> CEO, SandAI
• Yunbo Wang (2015-2020): CCF优秀博士学位论文奖 -> Associate Professor, Shanghai Jiao Tong University
• Zhongyi Pei (2015-2020): -> Assistant Researcher, Tsinghua University
• Ximei Wang (2017-2022): Co-supervised with Prof. Jianmin Wang. 北京市优秀毕业生 -> WeChat
• Xinyang Chen (2017-2022): 全球AI华人百强新星 -> Assistant Professor, Harbin Institute of Technology (Shenzhen)
• Yang Shu (2018-2023): -> Assistant Professor, East China Normal University
• Zhiyu Yao (2019-2024): -> Qiyuan Lab
• Yuchen Zhang (2020-2024): Co-supervised with Prof. Jianmin Wang ->
• Kaichao You (2020-2025): 字节跳动奖学金, 百度奖学金, 苹果学者 ->
• Haixu Wu (2020-2025): 清华大学特等奖学金, 清华大学研究生学术新秀 ->
• Yong Liu (2021-2026): ->
• Jincheng Zhong (2022-2026): ->
• Jialong Wu (2022-2027): ->
• Xingzhuo Guo (2023-2028): ->
• Yuezhou Ma (2024-2029): ->

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 -> Tencent
• Jue Wang (2015-2018): Co-supervised with Prof. Jianmin Wang -> Government @ Hunan
• 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): -> ByteDance
• Zhi Kou (2019-2022): -> Alimama
• Jincheng Zhong (2019-2022): -> THU PhD
• Jinghan Gao (2019-2022): -> Alibaba
• Junguang Jiang (2020-2023): 清华大学优秀硕士学位论文, 清华大学优秀硕士毕业生 -> Alimama
• Jiehui Xu (2020-2023): -> Government @ Shanghai
• Kaiyuan Chen (2021-2024): 清华大学优秀硕士学位论文 -> ByteDance
• Tengge Hu (2021-2024): 清华大学优秀硕士学位论文, 清华大学优秀硕士毕业生 -> China Southern Power Grid
• Haoyu Ma (2022-2025): ->
• Lanxiang Xing (2022-2025): ->
• Baixu Chen (2022-2025): ->
• Qilong Ma (2022-2025): ->
• Hang Zhou (2023-2026): ->
• Chaoyi Deng (2023-2026): ->
• Huakun Luo (2024-2027): ->
• Yu Zhang (2024-2027): ->
• Guo Qin (2024-2027): ->
• Haoran Zhang (2024-2027): ->

Undergraduates

• Zhangjie Cao (2014-2018): 全球AI华人百强新星 -> Stanford PhD
• Shichen Liu (2014-2018): 商汤奖学金 -> USC PhD
• Bin Liu (2014-2018): 商汤奖学金 -> 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): -> THU Master
• Junguang Jiang (2016-2020): -> THU Master
• Weirui Ye (2016-2020): -> THU PhD
• Tianle Liu (2016-2020): -> Harvard PhD
• Hong Liu (2017-2021): 清华大学特等奖学金, 清华大学优秀毕业生 -> Stanford PhD
• Yong Liu (2017-2021): 清华大学优秀毕业生, 北京市优秀本科生毕业论文 -> THU PhD
• Yifei Ji (2017-2021): 北京市优秀毕业生 -> THU Master
• Shijie Wang (2017-2021): -> Brown PhD
• Haoyu Niu (2017-2021): -> CMU Master
• Jialong Wu (2018-2022): 清华大学优秀毕业生 -> THU PhD
• Haoyu Ma (2018-2022): -> THU Master
• Lanxiang Xing (2018-2022): -> THU Master
• Baixu Chen (2018-2022): -> THU Master
• Chenyu Wang (2018-2022): 清华大学优秀毕业生 -> MIT PhD
• Hang Zhou (2019-2023): 清华大学优秀毕业生 -> THU Master
• Chaoyi Deng (2019-2023): -> THU Master
• Xingzhuo Guo (2019-2023): -> THU PhD
• Yiwen Qiu (2019-2023): 商汤奖学金 -> CMU PhD
• Yuezhou Ma (2020-2024): 清华大学优秀毕业生 -> THU PhD
• Huakun Luo (2020-2024): 清华大学优秀毕业生 -> THU Master
• Yu Zhang (2020-2024): 北京市优秀毕业生 -> THU Master
• Guo Qin (2020-2024): -> THU Master
• Haoran Zhang (2020-2024): -> THU Master

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