Jindong Wang

Assistant Professor, William & Mary
jwang80 [at] wm.edu, jindongwang [at] outlook.com
Integrated Science Center 2273, Williamsburg, VA
Google scholar | DBLP | Github | Twitter/X | LinkedIn | Zhihu | Bilibili || CV CV (Chinese)

Dr. Jindong Wang is a Tenure-Track Assistant Professor at William & Mary since 2025. Previously, he has been a Senior Researcher in Microsoft Research Asia for 5.5 years. His research interest includes machine learning, large language and foundation models, and AI for social science. Since 2022, he has been selected by Stanford University as one of the World’s Top 2% Scientists and one of the Most Influential AI Scholars by AMiner. He serves as the associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), guest editor for ACM Transactions on Intelligent Systems and Technology (TIST), area chair for ICML, NeurIPS, ICLR, KDD, ACMMM, and ACML, SPC of IJCAI and AAAI. He has published over 60 papers with 15000+ citations at leading conferences and journals such as ICML, ICLR, NeurIPS, TPAMI, IJCV etc. His research is reported by Forbes, MIT Technology Review, and other international media. He received best and outstanding papers awards at several internation conferences and workshops. He published a book Introduction to Transfer Learning. He gave tutorials at IJCAI’22, WSDM’23, KDD’23, AAAI’24, and AAAI’25. He leads several impactful open-source projects, including transferlearning, PromptBench, torchSSL, and USB, which received over 16K stars on Github. He obtained his Ph.D from University of Chinese Academy of Sciences in 2019 with the excellent PhD thesis award and a bachelor’s degree from North China University of Technology in 2014.

PhD application: 25 fall has been fulfilled. You can either apply for future PhDs or apply for Internship or collaboration. [PhD and interns] [Chinese version] []

Research interest: (See this page for more details)

Machine learning: To make ML systems more robust, trustworthy, and responsible. Related topics include: robust machine learning, OOD / domain generalization, transfer learning, semi-supervised learning, federated learning, and related applications. These days, I’m particularly interested in ML with foundation models.
Philosophy of language models: We mainly focus on understanding the potential and limitation of large foundation models. Related topics: LLM evaluation and enhancement, and agent behavior.
AI for social sciences: How to measure the impact of generative AI on different domains? How to assist interdisciplinary domains using powerful AI models? How to use existing social science knowledge to help us better understand AI behaviors?

News

Jan 10, 2025	Invited to be an Area Chair for KDD 2025.
Dec 16, 2024	Check my first Microsoft Research Podcast on our paper at NeurIPS’24: [Podcast]
Dec 15, 2024	Our work ZooPFL received Outstand Paper Award at NeurIPS 2024 workshop on federated foundation models!
Dec 07, 2024	Invited to be an area chair (AC) for ICML 2025 and senior program committee (SPC) for IJCAI 2025.
Sep 28, 2024	We have one collaborative paper accepted by NeurIPS 24 dataset and benchmark track as a spotlight! [paper]

Selected publications

Slight Corruption in Pre-training Data Makes Better Diffusion Models

Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang^# , and Bhiksha Raj

Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code Zhihu ]

(Spotlight)
Competeai: Understanding the competition behaviors in large language model-based agents

Qinlin Zhao, Jindong Wang^# , Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]

(Oral)
The good, the bad, and why: Unveiling emotions in generative ai

Cheng Li, Jindong Wang^# , Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie

International Conference on Machine Learning (ICML) 2024 | [ arXiv Code ]
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization

Wang Lu, Jindong Wang^# , Xinwei Sun, Yiqiang Chen, Xiangyang Ji, Qiang Yang, and Xing Xie

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2024 | [ arXiv Code Zhihu ]
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Kaijie Zhu, Jiaao Chen, Jindong Wang^# , Neil Zhenqiang Gong, Diyi Yang, and Xing Xie

International Conference on Learning Representation (ICLR) 2024 | [ arXiv Code ]

(Spotlight (Top 5%))
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks

Hao Chen, Jindong Wang^# , Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, and Bhiksha Raj

International Conference on Learning Representation (ICLR) 2024 | [ arXiv Code Zhihu ]

(Spotlight (Top 5%))

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 ]

(900+ citations; Top 17 most cited NeurIPS papers in the past 5 years)

@article{zhang2021flexmatch,
  title = {Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling},
  author = {Zhang, Bowen and Wang, Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume = {34},
  year = {2021},
  bibtex_show = {true},
  corr = {true},
  abbr = {NeurIPS},
  arxiv = {https://arxiv.org/abs/2110.08263},
  pdf = {http://jd92.wang/assets/files/flexmatch_nips21.pdf},
  code = {https://github.com/TorchSSL/TorchSSL},
  zhihu = {https://zhuanlan.zhihu.com/p/422930830},
  video = {https://www.youtube.com/watch?v=aYuUwyZl_WY},
  slides = {https://www.jianguoyun.com/p/DXeFVg8QjKnsBRibj54E},
  selected = {true},
  special = {900+ citations; Top 17 most cited NeurIPS papers in the past 5 years}
}

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 ]

(Paperdigest most influencial CIKM paper)

@inproceedings{du2021adarnn,
  title = {Adarnn: Adaptive learning and forecasting of time series},
  author = {Du, Yuntao and Wang, Jindong and Feng, Wenjie and Pan, Sinno and Qin, Tao and Xu, Renjun and Wang, Chongjun},
  booktitle = {The 30th ACM International Conference on Information \& Knowledge Management (CIKM)},
  pages = {402--411},
  year = {2021},
  bibtex_show = {true},
  abbr = {CIKM},
  corr = {true},
  selected = {true},
  arxiv = {https://arxiv.org/abs/2108.04443},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn},
  pdf = {cikm21-adarnn.pdf},
  special = {Paperdigest most influencial CIKM paper}
}

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 ]

(600+ citations; 2nd most cited paper in MM’18)

@inproceedings{wang2018visual,
  title = {Visual domain adaptation with manifold embedded distribution alignment},
  author = {Wang, Jindong and Feng, Wenjie and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yu, Philip S},
  booktitle = {The 26th ACM international conference on Multimedia},
  pages = {402--410},
  year = {2018},
  bibtex_show = {true},
  abbr = {ACMMM},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA},
  pdf = {a11_mm18.pdf},
  supp = {https://www.jianguoyun.com/p/DRuWOFkQjKnsBRjkr2E},
  poster = {poster_mm18.pdf},
  selected = {true},
  special = {600+ citations; 2nd most cited paper in MM'18}
}

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 ]

(600+ citations; Most cited paper in ICDM’17)

@inproceedings{wang2017balanced,
  title = {Balanced distribution adaptation for transfer learning},
  author = {Wang, Jindong and Chen, Yiqiang and Hao, Shuji and Feng, Wenjie and Shen, Zhiqi},
  booktitle = {IEEE international conference on data mining (ICDM)},
  pages = {1129--1134},
  year = {2017},
  organization = {IEEE},
  bibtex_show = {true},
  abbr = {ICDM},
  code = {https://github.com/jindongwang/transferlearning/tree/master/code/BDA},
  pdf = {a08_icdm17.pdf},
  html = {http://ieeexplore.ieee.org/document/8215613/?part=1},
  selected = {true},
  special = {600+ citations; Most cited paper in ICDM'17}
}