Jindong Wang

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

PhD and interns: You can apply for 2026 PhD or Internship or collaboration. [Chinese blog]

I am open to industry and university visit, consultant, and more collaboration!

Dr. Jindong Wang is an Assistant Professor at Department of Data Science, William & Mary. He is also a faculty member of Future of Life Institute. Previously, he was a Senior Researcher in Microsoft Research Asia from 2019 to 2024. His research interest spans machine learning, large foundation models, and generative AI for social science. He is among World’s Top 2% Highly Cited Scientists and Most Influential AI Scholars. He is 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, ACL, ACMMM, and ACML, and SPC of IJCAI and AAAI. He published over 60 papers at top-tier venues (23000+ citations, H-index 54). His research is supported by Amazon Research Award, Google Research Award, AMD University Program AI & HPC Award, Microsoft Accelerate Foundation Model Research Award, and William & Mary Faculty Research Award. His research was integrated into Microsoft health products that reduced token consumptions by 15%, and quant finance with increased prediction accuracy. His work was reported by Forbes, MIT Technology Review, and other international media. He received best papers awards at several conferences and workshops. He published a book Introduction to Transfer Learning and gave tutorials at IJCAI’22, WSDM’23, KDD’23, AAAI’24, AAAI’25, and CVPR’25.

News

Oct 22, 2025	We got the prestigious Amazon Research Award!
Oct 17, 2025	We received an unrestricted gift (award) from Google Deepmind!
Oct 16, 2025	We got the AMD University Program HPC Award!
Oct 13, 2025	Our NeurIPS 2025 workshop on LLM persona has been accepted!
Oct 07, 2025	We got the Google Cloud Research Credit award!
Sep 25, 2025	We got some Google Cloud credits for our research! Thanks to Google Cloud Teaching Credits Program.
Sep 18, 2025	Three papers got accepted by NeurIPS 2025. Congrats!
Sep 16, 2025	Our group received an academic compute grant from Modal!

Selected publications

Impact of Noisy Supervision in Foundation Model Learning

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

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2025 | [ arXiv Code ]
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}
}