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 || Resume
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. 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!
|Nov 28, 2022||I was invited to serve as a senior program member (SPC) of IJCAI 2023.|
|Nov 18, 2022||We will give a tutorial on domain/OOD generalization at WSDM 2023!|
|Sep 17, 2022||Paper USB: A Unified Semi-supervised Learning Benchmark is accepted by NeurIPS 2022! [arXiv] [Code]|
|Sep 7, 2022||Our paper Margin Calibration for Long-Tailed Visual Recognition is accepted by ACML 2022! [arXiv]|
|Aug 17, 2022||Paper Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction is accepted by COLING-22!|
|Aug 4, 2022||《迁移学习导论》第二版现已上市！主页|
- 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, Semi-supervised learning repo, and Personalized federated 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)
Generalizing to Unseen Domains: A Survey on Domain GeneralizationIEEE Transactions on Knowledge and Data Engineering (TKDE) 2022
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT) 2022
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly DetectionIEEE Transactions on Knowledge and Data Engineering (TKDE) 2022
ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing44th International Conference on Software Engineering (ICSE) 2022
Flexmatch: Boosting semi-supervised learning with curriculum pseudo labelingAdvances in Neural Information Processing Systems (NeurIPS) 2021
Adarnn: Adaptive learning and forecasting of time seriesThe 30th ACM International Conference on Information & Knowledge Management (CIKM) 2021
Visual domain adaptation with manifold embedded distribution alignmentThe 26th ACM international conference on Multimedia 2018 (300+ citations; 2nd most cited paper in MM’18)
Balanced distribution adaptation for transfer learning2017 IEEE international conference on data mining (ICDM) 2017 (300+ citations; most cited paper in ICDM’17)