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)
I’m currently a Senior Researcher at Microsoft Research Asia (MSRA), in a group managed by Xing Xie. Before joining MSRA, I obtained my Ph.D. from Institute of Computing Technology, Chinese Academy of Sciences in June, 2019. My doctoral thesis was awarded the excellent Ph.D. thesis of Chinese Academy of Sciences. In 2018/04–2018/08, I was a visitor of Prof. Qiang Yang’s group at Hong Kong University of Science and Technology (HKUST). My work on transfer learning won the best paper awards in ICCSE 2018 and FTL-IJCAI 2019. In 2021, I published the textbook Introduction to Transfer Learning, a hands-on introduction to transfer learning. In 2022, I was selected as one of the 2022 AI 2000 Most Influential Scholars by AMiner between 2012-2021 (ranked 49/2000). Four of my first-author papers are ranked by Google Scholar as highly-cited papers. I gave tutorials at IJCAI’22.
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) robustness. 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!
News
May 17, 2023
Two papers are accepted by KDD 2023.
May 4, 2023
The large model for large model evaluation “PandaLM” is released on Github! [PandaLM]
May 3, 2023
Our paper “GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective” is accepted by ACL 2023 findings! [paper]
May 1, 2023
We will give a tutorial on KDD 2023 named “Trustworthy machine learning: generalization, robustness, and interpretability”!
Apr 17, 2023
Two papers (FedCLIP and ChatGPT robustness) are accepted by ICLR 2023 workshop on robust large models! [Papers]
Apr 5, 2023
The English version of 迁移学习导论 is published online: [Springer]
Highlights
Four 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
| [
arXivPDF
]
@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}}
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
| [
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}}
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
]
(400+ 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={400+ 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 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={400+ citations; most cited paper in ICDM'17}}