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 currently works at Microsoft Research Asia as a Senior Researcher. 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. His research interest includes machine learning, large language models, and AI for social science. He has published over 50 papers with 12000+ citations at leading conferences and journals such as ICML, ICLR, NeurIPS, TPAMI, IJCV etc. His research is reported by Forbes and other international media. In 2023 and 2024, he was selected by Stanford University as one of the World’s Top 2% Scientists and one of the AI Most Influential Scholars by AMiner. He has several Google scholar highly cited papers, Huggingface featured papers, and paperdigest most influential papers. He received the best paper award at ICCSE’18 and IJCAI’19 workshop. 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 NeurIPS, ICLR, KDD, ACMMM, and ACML, senior program committee member of IJCAI and AAAI. He leads several impactful open-source projects, including transferlearning, PromptBench, torchSSL, USB, personalizedFL, and robustlearn, which received over 16K stars on Github. He published a book Introduction to Transfer Learning. He gave tutorials at IJCAI’22, WSDM’23, KDD’23, and AAAI’24.

Research interest: (See this page for more details)

Machine learning: I’m generally interested in designing algorithms and applications to make machine learning 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.
Large language models: We mainly focus on understanding the potential and limitation of large foundation models. Related topics: LLM evaluation and enhancement.
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?
Interested in internship or collaboration? Contact me. I’m experimenting a new form of research collaboration. You can click here if you are interested!

News

Sep 28, 2024	We have one collaborative paper accepted by NeurIPS 24 dataset and benchmark track as a spotlight! [paper]
Sep 26, 2024	We have 5 papers accepted by NeurIPS 2024, including a spotlight! Congrats to the students!
Sep 20, 2024	Our new work AgentReview: Exploring Peer Review Dynamics with LLM Agents is accepted by EMNLP main track! [paper]
Aug 30, 2024	Our vision paper “On Catastrophic Inheritance of Large Foundation Models” is accepted by DMLR! [arxiv]
Aug 20, 2024	Invited to be an Area Chair for ICLR 2025 and senior program member (SPC) of AAAI 2025.
Jul 2, 2024	Our paper “SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization” is accepted by ECCV 2024! [paper]

Highlights

6 of my papers are highly cited and ranked top 20 globally in recent 5 years in Google scholar metrics. See here. I also have 6 papers featured by Hugging Face.
I wrote a popular book Introduction to Transfer Learning to make it easy to learn, understand, and use transfer learning.
I lead the most popular transfer learning, semi-supervised learning, and LLM evaluation projects on Github: Transfer learning repo, Semi-supervised learning repo, PromptBench for LLM evaluation, Personalized federated learning repo.
I was selected as one of the World's Top 2% Scientist by Stanford and 2022 AI 2000 Most Influential Scholars by AMiner in 2023.

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 ]

(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%))
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 ]
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 ]
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 ]
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 ]

(500+ citations)
```
@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 = {500+ citations}
}
```
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 ]

(500+ 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 = {500+ 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 ]

(500+ 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 = {500+ citations; most cited paper in ICDM'17}
}
```