Jindong Wang (王晋东)
Researcher
Machine Learning Group, Microsoft Research Asia (MSRA)

Google Scholar | Microsoft Research | Github Zhihu Weibo | Wechat public account

Contact: Building 2, No. 5 DanLing Street, Haidian District, Beijing, China.
jindong.wang (at) microsoft.com
News
  • Internship: I'm looking for highly-motivated students to collaborate or for internship. (The new internship period starts in 2020/09.) Please send me your CVs if you're interested. See this article for more detail.
  • 2020-04-20: Paper Joint Partial Optimal Transport for Open Set Domain Adaptation has been accepted by IJCAI-20.
  • 2020-04: Our paper Deep Subdomain Adaptation Network for Image Classification has been accepted by IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • 2020-04: Our paper FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare has been accepted by IEEE Intelligent Systems (FedHealth was the Best Application Paper in IJCAI-19 federated learning workshop).
  • 2020-02: Our paper Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation has been accepted by CVPR 2020.
  • 2019-12: Give a detailed introduction to transfer learning for Tsinghua University's EE graduates: [Class photo].
  • 2019-10: Paper Cross-dataset activity recognition via adaptive spatial-temporal transfer learning has been accepted by UbiComp 2020.
Short Bio
I'm currently a researcher at machine learning group, Microsoft Research Asia (MSRA). Before joining MSRA, I obtained by Ph.D. from Institute of Computing Technology, Chinese Academy of Sciences in June, 2019. My Ph.D. thesis is mainly on transfer learning algorithms and applications in ubiquitous computing. At MSRA, my research interest is mainly about generalization problems, including but not limited to: transfer learning, few-shot learning, meta learning, adaptive learning, and related applications.
[Research] [Education] [Publications] [Talks] [Projects] [Awards & Service] [Skill] [Person]

Research
My main interest is the generalization-related problems. Generalization is perhaps the most fundamental and mysterious aspects of machine learning. Briefly speaking, it is the game of data, models, and optimizers, where each one of them plays a key role in helping a learning machine achieve better generalization abilities. Therefore, that machine can be non-trivially applied to a new environment with new tasks and datasets.
Such triumph would require efforts from many research areas, including but not limited to: deep learning, transfer learning, domain adaptation/generalization, out-of-distribution detection, meta learning, few-shot learning, adversarial robustness, adaptative learning, and so on. At the same time, these algorithms should be working with several applications to deal with real-world challenges, such as activity recognition, computer vision, financial technology, etc.
Research Experience
Education
Publications

Technical reports

  1. 迁移学习简明手册 (First manual in transfer learning). [Link] [PDF] [Github]
    Jindong Wang.
    Transfer Learning Tutorial 2018

Conference papers

  1. Joint Partial Optimal Transport for Open Set Domain Adaptation.
    Renjun Xu, Pelen Liu, Ying Zhang, Fang Cai, Jindong Wang, et al.
    To appear in International Joint Conference on Artificial Intelligence (IJCAI-20).
  2. Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation.
    Pelen Liu, Renjun Xu, Liyan Wang, Chao Chen, Jindong Wang.
    To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR-20).
  3. Cross-dataset Activity Recognition via Adaptive Spatial-temporal Transfer Learning. [PDF] [Link]
    Xin Qin, Yiqiang Chen, Jindong Wang, Chaohui Yu.
    To appear in ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-20).
  4. Transfer Learning with Dynamic Adversarial Adaptation Network. [PDF] [Code]
    Chaohui Yu*, Jindong Wang*, Yiqiang Chen, Meiyu Huang. (* denotes equal contribution)
    In Proceedings of IEEE International Conference on Data Mining (ICDM-19). Beijing, China. Nov. 8-11, 2019. Pages 778-786. (Full Paper, AC rate: 9.08%)
  5. DrowsyDet: A Mobile Application for Real-Time Driver Drowsiness Detection.
    Chaohui Yu, Xin Qin, Yiqiang Chen, Jindong Wang, Chenchen Fan.
    In Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC-19). Leicester, UK. Aug. 19-23, 2019. Pages 1-8. (Full paper)
  6. Easy Transfer Learning By Exploiting Intra-domain Structures. [PDF] [Code]
    Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang.
    In Proceedings of IEEE International Conference on Multimedia and Expo (ICME-19). Shanghai, China. Jul. 8-12, 2019. Pages 1-6. (Full paper)
  7. Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Prunning. [PDF] [Code]
    Chaohui Yu, Jindong Wang, Yiqiang Chen, Zijing Wu.
    In Proceedings of International Joint Conference on Neural Networks (IJCNN-19). Budapest, Hungary. July 14-19, 2019. Pages 1-8. (Full paper, Oral)
  8. Visual Domain Adaptation with Manifold Embedded Distribution Alignment. [PDF] [Code] [Supplementary] [Poster]
    Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, Philip S. Yu.
    In Proceedings of ACM International Conference on Multimedia (ACMMM-18). Seoul, Korea. Oct. 22-26, 2018. Pages 402-410. (Full paper, Oral, Top 10 papers)
  9. Deep Transfer Learning for Cross-domain Activity Recognition. [PDF] [Link]
    Jindong Wang, Vincent W. Zheng, Yiqiang Chen, Meiyu Huang.
    In Proceedings of The 3rd International Conference on Crowd Science and Engineering (ICCSE-18). Singapore. Jul. 31-Aug. 2, 2018. Pages 1-8. (Best Paper)
  10. Stratified Transfer Learning for Cross-domain Activity Recognition. [PDF] [Code]
    Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, Philip S. Yu.
    In Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom-18). Athens, Greece. Mar. 19-23, 2018. Pages 1-10. (Full paper)
  11. Balanced Distribution Adaptation for Transfer Learning. [Link] [PDF] [Code]
    Jindong Wang, Yiqiang Chen, Shuji Hao, Wenjie Feng, Zhiqi Shen.
    In Proceedings of IEEE International Conference on Data Mining (ICDM-17). New Orleans, USA. Nov. 18-21, 2017. Pages 1129-1134. (Short paper)
  12. OCEAN: A New Opportunistic Computing Model for Wearable Activity Recognition. [PDF] [Link]
    Yiqiang Chen, Yang Gu, Xinlong Jiang, Jindong Wang.
    In Proceedings of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-16). Heidelberg, Germany. Sep. 12-16, 2016.
  13. Less Annotation on Personalized Activity Recognition Using Context Data. [PDF] [Link]
    Lisha Hu, Yiqiang Chen, Shuangquan Wang, Jindong Wang, Jianfei Shen, Xinlong Jiang, Zhiqi Shen.
    In Proceedings of the 13th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC-16). Toulouse, France. Jul. 18-21, 2016. Pages 327-332.
  14. A Study of Players' Experiences during Brain Games Play. [PDF] [Link]
    Faizan Ahmad, Yiqiang Chen, Shuangquan Wang, Zhenyu Chen, Jianfei Shen, Lisha Hu, Jindong Wang.
    In Proceedings the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI-16). Phuket, Thailand. Aug. 22-26, 2016. (Full paper)

Journal articles

  1. Deep Subdomain Adaptation Network for Image Classification.
    Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He.
    To appear in IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS).
  2. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. [PDF] [Link]
    Yiqiang Chen, Xin, Qin, Jindong Wang, Chaohui Yu, Wen Gao.
    In IEEE Intelligent Systems.
  3. Transfer Learning with Dynamic Distribution Adaptation. [PDF] [Link]
    Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, Qiang Yang.
    In ACM Transactions on Intelligent Systems and Technology (ACM TIST). Volume 11, Issue 1, 2020. Pages 1-25.
  4. Cross-dataset Activity Recognition via Adaptive Spatial-temporal Transfer Learning. [PDF] [Link]
    Xin Qin, Yiqiang Chen, Jindong Wang, Chaohui Yu.
    In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Volume 3, Issue 4, 2019. Pages 1-25. (Ubicomp-20 publications)
  5. Transfer Channel Prunning for Deep Unsupervised Domain Adaptation. [Link] [PDF] [Code]
    Chaohui Yu, Jindong Wang, Yiqiang Chen, Xin Qin.
    In International Journal of Machine Learning and Cybernetics (IJMLC). Volume 10, Issue 11, 2019. Pages 3129-3144.
  6. Multi-representation Adaptation Network for Cross-domain Image Classification. [Link] [PDF] [Code]
    Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He.
    In Neural Networks (NEUNET). Volume 119, Nov. 2019. Pages 214-221.
  7. Cross-position Activity Recognition with Stratified Transfer Learning. [Link] [PDF]
    Yiqiang Chen, Jindong Wang, Meiyu Huang, Han Yu.
    In Pervasive and Mobile Computing (PMCJ). Volume 57, Jul. 2019. Pages 1-13
  8. Deep Learning for Sensor-based Activity Recognition: A Survey. [Link] [PDF]
    Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, Lisha Hu.
    In Pattern Recognition Letters (PRL). Volume 119, Mar. 2019. Pages 3-11.
  9. OKRELM: Online Kernelized and Regularized Extreme Learning Machine for Wearable-based Activity Recognition. [Link] [PDF]
    Lisha Hu, Yiqiang Chen, Jindong Wang, Chunyu Hu, Xinlong Jiang.
    In International Journal of Machine Learning and Cybernetics (IJMLC). Volume 9, Issue 9, Sep. 2018. Pages 1577–1590.

Workshop papers

  1. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. [PDF]
    Yiqiang Chen, Jindong Wang, Chaohui Yu, Wen Gao, Xin Qin.
    In IJCAI-19 Workshop on Federated Machine Learning. Macao, China. Aug. 10-16, 2019. Pages 1-8. (Best Application Paper)
  2. Transfer Channel Prunning for Deep Unsupervised Domain Adaptation. [Link] [PDF] [Code]
    Chaohui Yu, Jindong Wang, Yiqiang Chen, Xin Qin.
    In PAKDD-19 Workshop on Deep Learning for Knowledge Transfer. Macao, China. Apr. 2019.

Book chapters

  1. 保研一本通. 2014 [JD Link] [PDF]
Talks
Projects
  1. See my Github rankings all over the world: [Link]
  2. Leading the transfer learning tutorial (迁移学习简明手册) on Github: Tutorial
  3. Leading a pupular transfer learning resource projects on Github: Transfer Learning
  4. I'm also leading other popular research projects: Machine Learning, Activity Recognition
  5. Numberconverter: a simple wox plugin to convert numbers into certain systems. 2015
  6. My BMI & 2048 : a simple BMI app, while 2048 is a modified 2048 game about study. Available at Apple's App Store. 2015
  7. I started a software studio Pivot Studio with a group of talented people, and we made many applications. 2010-2014
Academic activities and awards
Academic service
Awards and Honors
Miscellaneous
Skills
Person