Jiliang Tang
Also known as Ji-Liang Tang
Jiliang Tang develops graph learning for social media, education and biology, with work on robust graph neural networks. He trained in Beijing and Arizona, worked at Yahoo Labs, and joined Michigan State University as a faculty member. His work includes a survey of fake-news detection, the Pro-GNN framework for robust graph learning, and a coauthored textbook on deep learning for graphs. The 2022 award recognized his contributions to graph learning, and a 2025 project with Hui Liu aims to improve graph data for reliable AI. As of October 2026, he is MSU Research Foundation Distinguished Professor in Computer Science and Engineering.
Timeline
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2008–2015
Training in computing
He earns a BE and MS from Beijing Institute of Technology in 2008 and 2010, then completes his PhD at Arizona State University in 2015.
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2015–2022
From Yahoo to MSU
He works as a research scientist at Yahoo Labs from 2015 to 2016, joins MSU as an assistant professor in 2016, and becomes associate professor in 2021 and professor in 2022.
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2017
Surveying fake news
With Kai Shu, Amy Sliva, Suhang Wang and Huan Liu, he reviews fake-news characterization, detection methods, metrics, datasets and open questions for social media.
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2020
Building Pro-GNN
With five coauthors, he introduces Pro-GNN, a framework that jointly learns graph structure and a robust graph neural network from perturbed data.
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2021
A guide to graph learning
With Yao Ma, he coauthors Deep Learning on Graphs, a 2021 book covering graph foundations, neural network methods and applications.
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2022
An award for graph learning
He receives the J. K. Aggarwal Prize for work on graph learning and applications of graph networks in social media, education and biology.
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2025
Improving graph data quality
With Hui Liu, he works on a four-year $300,000 project to make graph data more compact, clean and aligned for efficient, accurate and robust AI systems across healthcare, finance and national security.
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As of October 2026
A professor in graph AI
He is MSU Research Foundation Distinguished Professor in Computer Science and Engineering and works in the Data Science and Engineering Lab. His research spans graph neural networks, trustworthy AI, education and biology.
Sources and credits
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