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 | Zhihu | Wechat | Bilibili || CVCV (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 Yangâs 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. He has published over 50 papers with 6900 citations at leading conferences and journals such as ICLR, NeurIPS, TKDE, TASLP etc. He has 6 highly cited papers in Google Scholar metrics. His paper âFedHealthâ received the best application paper award at IJCAI FL workshop and it is the most cited paper among all federated learning for healthcare papers. He also received other awards including best paper award at ICCSEâ18 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 2012-2022. He serves as the senior program committee member of IJCAI and AAAI, and PC members for top conferences like ICML, NeurIPS, ICLR, CVPR 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 IJCAIâ22, WSDMâ23, and KDDâ23.
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, Iâm particularly interested in Large Language Models (LLMs) evaluation and robustness enhancement. See this page for more details. Interested in internship or collaboration? Contact me.
Announcement: Iâm experimenting a new form of research collaboration. You can click here if you are interested!
News
Jul 26, 2023
Paper Exploring Vision-Language Models for Imbalanced Learning has been accepted by IJCV! [paper] [code]
Jul 18, 2023
Paper Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning is accepted by ICCV 2023!
Jul 9, 2023
Paper MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation is accepted by IEEE TNNLS! [Paper]
Jul 7, 2023
We present the first survey on Evaluation of large language models! [arxiv] [code]
Jun 17, 2023
Paper Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution is accepted by KDD 2023 workshop on Causal Discovery, Prediction and Decision. [arxiv]
Jun 7, 2023
PromptBench: a unified benchmark to evaluate the adversarial robustness of prompts of large language models! [arxiv] [code]
Highlights
6 of my papers are highly cited and ranked top 20 globally in recent 5 years in Google scholar metrics. See here.
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
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
@article{wang2022generalizing,title={Generalizing to Unseen Domains: A Survey on Domain Generalization},author={Wang, Jindong and Lan, Cuiling and Liu, Chang and Ouyang, Yidong and Qin, Tao and Lu, Wang and Chen, Yiqiang and Zeng, Wenjun and Yu, Philip S.},journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},year={2022},bibtex_show={true},abbr={TKDE},arxiv={https://arxiv.org/abs/2103.03097},code={https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG},slides={DGtutorial_ijcai22.pdf},selected={true},pdf={DG_survey_TKDE22.pdf},website={https://dgresearch.github.io/}}
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
| [
arXivPDFCode
]
@article{lu2022semantic,title={Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition},author={Lu, Wang and Wang, Jindong and Chen, Yiqiang and Pan, Sinno and Hu, Chunyu and Qin, Xin},journal={Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e., UbiComp)},year={2022},abbr={IMWUT},bibtex_show={true},corr={true},selected={true},arxiv={http://arxiv.org/abs/2206.06629},pdf={imwut22-sdmix.pdf},code={https://github.com/microsoft/robustlearn}}
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
| [
arXivPDFCode
]
@article{zhang2022adaptive,title={Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection},author={Zhang, Yuxin and Wang, Jindong and Chen, Yiqiang and Yu, Han and Qin, Tao},journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},year={2022},abbr={TKDE},bibtex_show={true},corr={true},selected={true},arxiv={https://arxiv.org/abs/2201.00464},pdf={tkde22_amsl.pdf},code={https://github.com/zhangyuxin621/AMSL}}
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
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PDFCodeVideoZhihu
]
@inproceedings{zhang2022remos,title={ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing},author={Zhang, Ziqi and Li, Yuanchun and Wang, Jindong and Liu, Bingyan and Li, Ding and Chen, Xiangqun and Guo, Yao and Liu, Yunxin},booktitle={44th International Conference on Software Engineering (ICSE)},year={2022},bibtex_show={true},abbr={ICSE},pdf={icse22-remos.pdf},code={https://github.com/jindongwang/transferlearning/tree/master/code/deep/ReMoS},zhihu={https://zhuanlan.zhihu.com/p/446453487},video={https://www.bilibili.com/video/BV1mi4y1C7bP},selected={true}}
Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
@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={300+ 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
| [
arXivPDFCode
]
@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}}
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
| [
PDFSuppCodePoster
]
(500+ citations; 2nd most cited paper in MMâ18)
@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
| [
HTMLPDFCode
]
(400+ citations; most cited paper in ICDMâ17)
@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={400+ citations; most cited paper in ICDM'17}}