Jiliang Tang

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. He was the recipient of 2021 IEEE Big Data Security Junior Research Award, 2020 ACM SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, and 7 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 ACM Books). He has published his research in highly ranked journals and top conference proceedings, which have received tens of thousands of citations with h-index 62 (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
Adversarial Attacks and Defenses, Fairness, Trustworthy AI
Large-scale Data Learning and Optimization
AI for Education

News in 2021( More)

So humbled and grateful to receive this special gift from my current and formal students in the Chinese Teacher's Day!!!
Received the Rising Star Award from Association of Chinese Scholars in Computing
Our new book: Deep Learning on Graphs. (English Version for Pre-order with the Discount Code 101095) , (Chinese Version for Sale)
News from MSU Today: Decoding crop genetics with artificial intelligence
Received the IEEE Big Data Security Junior Research Award
Welcome four new professors from my group: Yao Ma (NJIT), Wenqi Fan (PolyU@HK), Hamid Karimi (Utah State) and Xiangyu Zhao (CityU@HK). They are recruiting students and feel free to contact them.
Invited to serve as the PC Chair of WSDM2022. Call for Paper

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
Is Homophily a Necessity for Graph Neural Networks?, arXiv:2106.06134
Towards the Memorization Effect of Neural Networks in Adversarial Training, arXiv:2106.04794
Automated Self-Supervised Learning for Graphs, arXiv:2106.05470
Graph Feature Gating Networks, arXiv:2105.04493
A Unified View on Graph Neural Networks as Graph Signal Denoising, arXiv:2010.01777
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
Memory-efficient Embedding for Recommendations, arXiv:2006.14827
Non-IID Graph Neural Networks, arXiv:2005.12386
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations , arXiv:2002.11252

Recent Publications ( Full List)

Educational Research in the Twenty-First Century: Leveraging Big Data to Explore Teachers’ Professional Behavior and Educational Resources Accessed within Pinterest , The Elementary School Journal 2021
Graph Adversarial Attack via Rewiring , KDD2021
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection, KDD2021
Automated Loss Function Search in Recommendations , KDD2021
To be Robust or to be Fair: Towards Fairness in Adversarial Training , ICML2021
Elastic Graph Neural Networks , ICML2021
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification , ACL2021
Multi-task Learning Based Online Dialogic Instruction Detection with Pre-trained Language Models , AIED2021
User Simulation via Supervised Generative Adversarial Network , WWW2021
Field-aware Embedding Space Searching in Recommender Systems , WWW2021
Linear Convergent Decentralized Optimization with Compression , ICLR2021
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily , SDM2021
Online Advertising Impression Strategy in Recommender Systems , AAAI2021
DeepRobust: A Platform for Adversarial Attacks and Defenses , AAAI2021
Node Similarity Preserving Graph Convolutional Networks , WSDM2021
CopyAttack: Attacking Black-box Recommendations via Copying Cross-domain User Profiles , ICDE2021
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study., SIGKDD Explorations, 2021