Jiliang Tang

Jiliang Tang is an assistant professor in the computer science and engineering department at Michigan State University since Fall@2016. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015. His research interests include social computing, data mining and machine learning and their applications in education. He was the recipient of 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, WSDM and SDM) and journal editors (e.g., TKDD). He has published his research in highly ranked journals and top conference proceedings, which have received tens of thousands of citations with h-index 56 (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

• Feature Selection, Graph Neural Networks, Network Representation Learning, Network Analysis, Recommendations
• Limited and Large-scale Data Learning and Optimization
• Security and Privacy in Machine Learning
• AI for Education, AI Ethics

Recent News ( More)

• My student Yao Ma is looking for a tenure-track assistant professor position. In addition to publish prolifically, he has wrote the first book in the frontier topic Deep Learning on Graphs with Cambridge Press.
• Our book: Deep Learning on Graphs
• Received the ACM SIGKDD Rising Star Award that aims to celebrate the early accomplishments of the SIGKDD communities' brightest new minds. Official News, MSU News, An Interview in Chinese
• KDD tutorial: Adversarial Attacks and Defenses: Frontiers, Advances and Practice
• KDD tutorial: Recent Advances in Multimodal Educational Data Mining in K-12 Education
• My student Tyler Derr will join Vanderbilt University as a Tenure-Track Assistant Professor in EECS starting Fall20!!!
• My student Yao Ma Won the Outstanding Graduate Student Award
• DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
• Won 2020 Distinguished Withrow Research Award
• MSU receives $1.2M NSF grant to study intelligent social network interventions
• My book "trust in social media" is one of the most read books in Synthesis Lectures on Information Security,Privacy, and Trust
• News Press It's possible to reverse-engineer AI chatbots to spout nonsense, smut or sensitive information
• Tutorial on graph neural networks at AAAI2020. Slides
• Workshop on Artificial Intelligence for Education at AAAI2020.
• Early Career Inivted Talks from IJCAI2019.
• Workshop about Deep Reinforcement Learning for Knowledge Discoveray at KDD2019.
• Best paper (or poster) awards (or shortlist) from ICHI2019, SECON2019 and SDM2019.
• Received the NSF Career Awards. Research@MSU , MSU Today, and News of Colledge of Engineering MSU

Recent Preprints

• A Unified View on Graph Neural Networks as Graph Signal Denoising, arXiv:2010.01777
• To be Robust or to be Fair: Towards Fairness in Adversarial Training, arXiv:2010.06121
• Linear Convergent Decentralized Optimization with Compression , arXiv:2007.00232
• Self-supervised Learning on Graphs: Deep Insights and New Directions, arXiv:2006.10141
• Yet Meta Learning Can Adapt Fast, It Can Also Break Easily, arXiv:2009.01672
• 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
• Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study., arxiv:2003.00653.
• Attacking Graph Convolutional Networks via Rewiring , arXiv:1906.03750.

Recent Publications ( Full List)

• Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning , EMNLP2020
• Graph Pooling with Representativeness, ICDM2020
• Learning from Incomplete Labeled Data via Adversarial Data Generation, ICDM2020
• Graph Convolutional Networks against Degree-Related Biases, CIKM2020
• Whole-Chain Recommendations, CIKM2020
• Graph Structure Learning for Robust Graph Neural Networks, KDD2020
• XGNN: Towards Model-Level Explanations of Graph Neural Networks, KDD2020
• Jointly Learning to Recommend and Advertise, KDD2020
• Automated Embedding Size Search in Deep Recommender Systems, SIGIR2020
• Streaming Graph Neural Networks, SIGIR2020
• Understanding and Promoting Teacher Connections in Online Social Media: A Case Study on Pinterest , L@S2020
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms, AIED2020
• Opening the Black Box: Interpretable Machine Learning for Geneticists, Trends in Genetics
• Mixed and blended emotional reactions to 2014 Ebola outbreak, Journal of Global Health
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network, WWW2020
• ROSE: Role-based Signed Network Embedding, WWW2020
• A Double Residual Compression Algorithm for Efficient Distributed Learning, AISTATS2020
• Global-and-Local Aware Data Generation for the Class Imbalance Problem , SDM2020
• Learning Multi-level Dependencies for Robust Word Recognition, AAAI2020
• Graduate Employment Prediction with Bias, AAAI2020
• Epidemic Graph Convolutional Network, WSDM2020
• Adversarial Attacks and Defenses in Images, Graphs and Text: A Review , International Journal of Automation and Computing (IJAC).