Jindong Wang

Senior Researcher, Microsoft Research Asia
Building 2, No. 5 Danling Street, Haidian District, Beijing, China
jindongwang [at] outlook.com, jindong.wang [at] microsoft.com
Google scholar | DBLP | Github || Twitter/X | Zhihu | Wechat | Bilibili || CV CV (Chinese)

Dr. Jindong Wang is currently a Senior Researcher at Microsoft Research Asia. He obtained his Ph.D from Institute of Computing Technology, Chinese Academy of Sciences in 2019. He visited Qiang Yang’s group at Hong Kong University of Science and Technology in 2018. His research interest includes robust machine learning, transfer learning, semi-supervised learning, and federated learning. His recent interest is large language models. He has published over 50 papers with 7000 citations at leading conferences and journals such as ICLR, NeurIPS, TKDE, TASLP etc. He has 6 highly cited papers according to Google Scholar metrics. He received the best paper award at ICCSE’18 and IJCAI’19 federated learning workshop and the prestigous excellent Ph.D thesis award (only 1 at ICT each year). In 2022 and 2023, he was selected as one of the AI 2000 Most Influential Scholars by AMiner between 2013-2023. He serves as the senior program committee member of IJCAI and AAAI, and reviewers for top conferences and journals like ICML, NeurIPS, ICLR, CVPR, TPAMI, AIJ etc. He opensourced several projects to help build a better community, such as transferlearning, torchSSL, USB, personalizedFL, and robustlearn, which received over 12K stars on Github. He published a textbook Introduction to Transfer Learning to help starters quickly learn transfer learning. He gave tutorials at IJCAI’22, WSDM’23, and KDD’23.

Research interest: robust machine learning, out-of-distribution / domain generalization, transfer learning, semi-supervised learning, federated learning, and related applications such as activity recognition and computer vision. These days, I’m particularly interested in Large Language Models (LLMs) evaluation and enhancement. See this page for more details. Interested in internship or collaboration? Contact me.

Announcement: I’m experimenting a new form of research collaboration. You can click here if you are interested!

Announcement: Call for papers for ACM TIST special issue on Evaluations of Large Langauge Models! [more]


Nov 26, 2023 Our paper “UP-Net: An Uncertainty-Driven Prototypical Network for Few-Shot Fault Diagnosis” is accepted by IEEE TNNLS!
Nov 21, 2023 Our paper “FIXED: Frustratingly Easy Domain Generalization with Mixup” is accepted by Conference on Parsimony and Learning (CPAL) 2023! [arxiv]
Nov 1, 2023 Paper Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition is accepted by UbiComp 2024! [arxiv]
Oct 27, 2023 Our new work CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents is released on ArXiv. [paper]
Oct 13, 2023 I’m selected as one of the World’s Top 2% Scientists by Stanford University! [news]
Oct 8, 2023 Our paper “Out-of-Distribution Generalization in Text Classification: Past, Present, and Future” is accepted by EMNLP 2023! [paper]


  1. 6 of my papers are highly cited and ranked top 20 globally in recent 5 years in Google scholar metrics. See here.
  2. I wrote a popular book Introduction to Transfer Learning to make it easy to learn, understand, and use transfer learning.
  3. I lead the most popular transfer learning and semi-supervised learning projects on Github: Transfer learning repo, Semi-supervised learning repo, and Personalized federated learning repo.
  4. I was selected into the list of 2022 AI 2000 Most Influential Scholars by AMiner in recognition of my contributions in the field of multimedia between 2012-2021 (ranked 49/2000)

Selected publications

  1. Domain-Specific Risk Minimization for Out-of-Distribution Generalization
    Yi-Fan Zhang, Jindong Wang# , Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng Tao, and Xing Xie
    The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023 | [ arXiv Code Video Zhihu ]
  2. Out-of-distribution Representation Learning for Time Series Classification
    Wang Lu, Jindong Wang# , Xinwei Sun, Yiqiang Chen, and Xing Xie
    International Conference on Learning Representations (ICLR) 2023 | [ arXiv Code Website Zhihu ]
  3. FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
    Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang# , Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, and Xing Xie
    International Conference on Learning Representations (ICLR) 2023 | [ arXiv Code Website Zhihu ]
  4. Generalizing to Unseen Domains: A Survey on Domain Generalization
    Jindong Wang , Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S. Yu
    IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022 | [ arXiv PDF Code Slides Website ]
  5. Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
    Wang Lu, Jindong Wang# , Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin
    Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e., UbiComp) 2022 | [ arXiv PDF Code ]
  6. Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
    Yuxin Zhang, Jindong Wang# , Yiqiang Chen, Han Yu, and Tao Qin
    IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022 | [ arXiv PDF Code ]
  7. ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing
    Ziqi Zhang, Yuanchun Li, Jindong Wang , Bingyan Liu, Ding Li, Xiangqun Chen, Yao Guo, and Yunxin Liu
    44th International Conference on Software Engineering (ICSE) 2022 | [ PDF Code Video Zhihu ]
  8. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
    Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang# , Manabu Okumura, and Takahiro Shinozaki
    Advances in Neural Information Processing Systems (NeurIPS) 2021 | [ arXiv PDF Code Slides Video Zhihu ]
    (300+ citations)
  9. Adarnn: Adaptive learning and forecasting of time series
    Yuntao Du, Jindong Wang# , Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang
    The 30th ACM International Conference on Information & Knowledge Management (CIKM) 2021 | [ arXiv PDF Code ]
  10. Visual domain adaptation with manifold embedded distribution alignment
    Jindong Wang , Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S Yu
    The 26th ACM international conference on Multimedia 2018 | [ PDF Supp Code Poster ]
    (500+ citations; 2nd most cited paper in MM’18)
  11. Balanced distribution adaptation for transfer learning
    Jindong Wang , Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen
    IEEE international conference on data mining (ICDM) 2017 | [ HTML PDF Code ]
    (400+ citations; most cited paper in ICDM’17)
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