Jindong Wang
Assistant Professor at Future of Life Institute (FLI)
Williamsburg, Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolJindong Wang is an Assistant Professor of Data Science at William & Mary and a faculty member at the Future of Life Institute, bringing 11 years of research experience at the intersection of machine learning, large foundation models, and generative AI for social science. Formerly a Senior Researcher at Microsoft Research Asia, he has published over 60 top-tier papers, amassed 23,000+ citations (H-index 54), and is ranked among the world’s top 2% highly cited scientists and most influential AI scholars. His work spans transfer learning, domain adaptation, robust ML and out-of-distribution generalization, and has been productized at Microsoft to reduce token consumption and improve quantitative finance predictions. An active contributor to open-source teaching resources, he maintains a widely used transfer learning repository and the LaTeX source for a practical tutorial book, reflecting his commitment to reproducible research and education. He serves as associate editor for TNNLS, guest editor for ACM TIST, and frequently chairs major conferences (ICML, NeurIPS, ICLR, KDD, ACL), underscoring his leadership in the field. Outside publications and awards, his research has attracted industry funding from Amazon, Google, AMD, and Microsoft and coverage in Forbes and MIT Technology Review.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD CS, Doctor of Philosophy - PhD CS at University of Chinese Academy of SciencesBachelor's degree Computer Science, Bachelor's degree Computer Science at North China University of TechnologyHong Kong University of Science and Technology (HKUST)
Stackoverflow
1reputation
0reached
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Github Skills (9)
content-management-system10
transfer-learning10
machine-learning10
latex10
matlab10
content-management10
adaptation10
documentation10
technical-writing10
Programming languages (11)
Github contributions (5)
jindongwang/transferlearning-tutorial
Apr 2018 - May 2021
《迁移学习简明手册》LaTex源码
Role in this project:
Technical Writer
Technical WriterContributions:3 releases, 58 commits, 11 PRs in 3 years 2 months
Contributions summary:Jindong primarily contributed to correcting errors and improving the content of the "transferlearning-tutorial" repository, which is a LaTeX source for a transfer learning tutorial. Their contributions included fixing typos, correcting formulas, clarifying descriptions, and updating the version number. The user also added links to purchase the book and to a知乎介绍.
latextransfer-learningdomain-adaptation
jindongwang/transferlearning
Jul 2017 - Jan 2023
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Role in this project:ML Engineer
ML EngineerContributions:5 reviews, 881 commits, 83 PRs in 5 years 7 months
Contributions summary:Jindong contributed several Matlab implementations of transfer learning algorithms like TCA, JDA, GFK, TJM, CORAL, and BDA, which are central to the repository's focus on domain adaptation. The commits involved implementing the core logic of these algorithms and kernel methods. Furthermore, the user added a T-SNE visualization utility, facilitating the analysis and understanding of feature representations learned by these methods.
multi-task-learningtransfer-learningtransferlearningdomain-adaptationsurvey
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