Jindong Wang

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 | Github | DBLP || Zhihu | Weibo | Wechat | Bilibili || Resume

I’m currently a researcher at Microsoft Research Asia (MSRA). 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 has won the best paper awards in ICCSE 2018 and FTL-IJCAI 2019. In 2021, I published the textbook 迁移学习导论, a hands-on introduction to transfer learning. In 2022, 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).

Research interest: transfer learning, out-of-distribution / domain generalization, semi-supervised learning, federated learning, and related applications such as activity recognition and computer vision. Never stop looking for highly self-motivated students for internship or collaboration.

News

May 22, 2022	Our domain generalization survey paper was accepted in IEEE TKDE! [Paper]
May 19, 2022	Our personalized federated learning paper was accepted in IEEE TBD! [Paper]
Apr 15, 2022	One paper accepted by Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)!
Apr 11, 2022	I will give a tutorial on domain generalization at IJCAI-ECAI 2022 on July. [Website]

Highlights

Four of my papers are highly cited and ranked top 20 globally in recent 5 years in Google scholar metrics! See here.
I wrote a popular book 迁移学习导论 to make it easy to learn, understand, and use transfer learning.
I lead the most popular transfer learning and semi-supervised learning projects on Github: Transfer learning repo and Semi-supervised learning repo
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)

Preprints

Yidong Wang, Bowen Zhang, Wenxin Hou, Zhen Wu, Jindong Wang^#, Takahiro Shinozaki. Margin Calibration for Long-Tailed Visual Recognition. arXiv preprint arXiv:2112.07225. [arXiv]
Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu. Learning Invariant Representations across Domains and Tasks. arXiv preprint arXiv:2103.05114. [arXiv]
Chaohui Yu, Jindong Wang^#, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, and Tie-Yan Liu. Learning to match distributions for domain adaptation. arXiv preprint arXiv:2007.10791. [arXiv]

Selected publications

ICSE
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

Bib PDF Code Video Zhihu
@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/ReMoS_artifact}, zhihu = {https://zhuanlan.zhihu.com/p/446453487}, video = {https://www.bilibili.com/video/BV1mi4y1C7bP}, selected = {true} }
NeurIPS
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 Bib PDF Code Slides Video Zhihu
@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} }
CIKM
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 Bib PDF Code
@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} }
ACMMM
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 (300+ citations; 2nd most cited paper in MM’18)

Bib PDF Supp Code Poster
@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 = {300+ citations; 2nd most cited paper in MM'18} }
ICDM
Balanced distribution adaptation for transfer learning

Jindong Wang , Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen

2017 IEEE international conference on data mining (ICDM) 2017 (300+ citations; most cited paper in ICDM’17)

Bib HTML PDF Code
@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 = {2017 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 = {300+ citations; most cited paper in ICDM'17} }

IMWUT
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 2022

Bib
@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}, year = {2022}, abbr = {IMWUT}, bibtex_show = {true}, corr = {true}, selected = {true} }
TKDE
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

arXiv Bib PDF
@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} }