Jiliang Tang
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Jiliang Tang is an associate professor (assistant professor, 2016-2021) in the computer science and engineering department at Michigan State University. Before that, he was a research scientist in Yahoo Research. He got his PhD from Arizona State University in 2015 under Dr. Huan Liu and MS and BE from Beijing Institute of Technology in 2010 and 2008, respectively. His research interests include social computing, data mining and machine learning and their applications in education and biology. He was the recipient of 2021 IEEE ICDM Tao Li Award, 2021 IEEE Big Data Security Junior Research Award, 2020 ACM SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, and 8 best paper awards (or runner-ups) including WSDM2018 and KDD2016. His dissertation won the 2015 KDD Best Dissertation runner up and Dean's Dissertation Award. He serves as conference organizers (e.g., KDD, SIGIR, WSDM and SDM) and journal editors (e.g., TKDD and TKDE). He has published his research in highly ranked journals and top conference proceedings, which have received tens of thousands of citations with h-index 67 (Google Scholar) and extensive media coverage (Links). Email: tangjili at msu dot edu Office: Engineering Building 2148 Mail: 428 S Shaw Ln Rm 3115, East Lansing, MI 48824 Lab: Data Science and Engineering Lab (Webpage, and Twitter ) |
Research Interests
- Graph Neural Networks, Deep Learning on Graphs
- Trusworthy AI: Safety, Robustness and Fairness
- AI+X: Education and Biology
News in 2022( More)
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Received the SIAM/IBM Early Career Research Award
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Received the Amazon Faculty Award
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My student Xiaorui Liu will join NC State as a tenure-track assisstant professor. He is the sixth student from my lab becoming faculty
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Received the Cisco Faculty Award
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Serve as an Associate Editor of IEEE TKDE
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Our new book: Deep Learning on Graphs. (English Version for Sale from Amazon) , (Chinese Version for Sale)
Recent Preprints
- Decentralized Composite Optimization with Compression , arXiv:2108.04448
- Imbalanced Adversarial Training with Reweighting, arXiv:2107.13639
- Trustworthy AI: A Computational Perspective, arXiv:2107.06641
- Graph Neural Networks for Multimodal Single-Cell Data Integration, arXiv:2203.01884
- An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering, arXiv:2112.07191
- Towards the Memorization Effect of Neural Networks in Adversarial Training, arXiv:2106.04794
- DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses, arXiv:2005.06149
- Self-supervised Learning on Graphs: Deep Insights and New Directions, arXiv:2006.10141
- Non-IID Graph Neural Networks, arXiv:2005.12386
Recent Publications ( Full List)
- Graph Trend Filtering Networks for Recommendations, SIGIR, 2022
- Toward Annotator Group Bias in Crowdsourcing, ACL, 2022
- Graph Condensation for Graph Neural Networks, ICLR, 2022
- Automated Self-Supervised Learning for Graphs, ICLR, 2022
- Is Homophily a Necessity for Graph Neural Networks, ICLR, 2022
- Rating Distribution Calibration for Selection Bias Mitigation in Recommendations, WWW, 2022
- Learning from Imbalanced Crowdsourced Labeled Data, SDM, 2022
- Localized Graph Collaborative Filtering, SDM, 2022