Jiliang Tang
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Jiliang Tang is a University Foundation Professor in the computer science and engineering department at Michigan State University. He was an associate professor (2021-2022) and an assistant professor (2016-2021) in the same department. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015 under Dr. Huan Liu. His research interests include social computing, data mining and machine learning and their applications in education and biology. He was the recipient of various awards including 2022 IAPR J. K. AGGARWAL Award, 2022 SIAM/IBM Early Career Research Award, 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). 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 79 (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)
My student Han Xu is on the job market for a TT AP Position. He helped build the research area of trustworthy AI in my group. He taught me everything I knew about trustworthy AI; thus I always considered him as my colleague.
My Student Wei Jin is on the job market for a TT AP Position. He is the best and genuinely exceptional!!!!.
Won the second place at NeurIPS’22 OGB-LSC MAG240M Track.
won a Kaggle Silver Medal at NeurIPS’22 Multimodal Single-Cell Integration (Top 2% ≈ 24/1266)
Release the Python toolkit DANCE for analyzing single-cell data via deep learning.
Received the J. K. AGGARWAL Award from the International Association for Pattern Recognition (IAPR).
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Promoted to Full Professor and Appointed as MSU Foundation Professor
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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
- Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation , arXiv:2302.03038
- Generative Diffusion Models on Graphs: Methods and Applications , arXiv:2302.02591
- Deep Learning in Single-Cell Analysis, arXiv:2210.12385
- Whole Page Unbiased Learning to Rank, arXiv:2210.10718
- Towards Fair Classification against Poisoning Attacks, arXiv:2210.09503
- Probabilistic Categorical Adversarial Attack & Adversarial Training, arXiv:2210.09364
- DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis, bioRxiv
- Test-Time Training for Graph Neural Networks, arXiv:2210.08813
- Alternately Optimized Graph Neural Networks, arXiv:2206.03638
- Defense Against Gradient Leakage Attacks via Learning to Obscure Data, arXiv:2206.03638
- Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?, arXiv:2205.10652
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability, arXiv:2204.08570
- Graph Enhanced BERT for Query Understanding, arXiv:2204.06522
- Decentralized Composite Optimization with Compression , arXiv:2108.04448
- 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