Jiaqi Ma

Trustworthy machine learning, regulatable machine learning; data-centric AI; machine learning on graph-structured data; information retrieval, recommender systems.

Gary M. Olson Outstanding Student Award, University of Michigan

Jiaqi Ma is an assistant professor at the University of Illinois Urbana-Champaign School of Information Sciences. His research focuses on trustworthy machine learning, graph machine learning, and recommender systems. His work has resulted 20+ publications in top AI conferences and journals, including ICML, NeurIPS, ICLR, KDD, AISTATS, JMLR, and TMLR. He was one of the leading organizers of the Workshop on Regulatable ML at NeurIPS, as well as three editions of the Workshop on Graph Learning Benchmarks at KDD and WebConf. His research on recommender systems has been leveraged into production systems in multiple large tech companies, such as Google and Alibaba. Prior to Illinois, Ma earned his PhD from the University of Michigan and worked as a postdoctoral researcher at Harvard University.

• Fall 2024 - Independent Study - IS589JM
• Spring 2025 - Concepts of Machine Learning - IS327A
• Spring 2025 - Network Analysis - IS527AC

Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju. Towards Bridging the Gaps Between the Right to Explanation and the Right to be Forgotten. ICML 2023.

Yutong Xie, Ziqiao Xu, Jiaqi Ma, Qiaozhu Mei. How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules. ICLR 2023.

Jiaqi Ma, Xingjian Zhang, Hezheng Fan, Jin Huang, Tianyue Li, Ting Wei Li, Yiwen Tu, Chenshu Zhu, Qiaozhu Mei. Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks. LOG 2022.

Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu. SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks. JMLR 2022.

Jiaqi Ma, Junwei Deng, Qiaozhu Mei. Subgroup Generalization and Fairness of Graph Neural Networks. NeurIPS 2021.

Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei. Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. AISTATS 2021.

Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei. Towards More Practical Adversarial Attacks on Graph Neural Networks. NeurIPS 2020.

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi. Off-policy Learning in Two-stage Recommender Systems. The WebConf 2020.

Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei. A Flexible Generative Framework for Graph-based Semi-supervised Learning. NeurIPS 2020.

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed H. Chi. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. KDD 2018.

"Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks." Invited talk at Amazon, 2023.

"Towards Trustworthy Machine Learning on Graph Data." Invited talk at CSAAW Seminar, University of Michigan, 2022.

"Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model." Invited talk at the Theory Seminar, Rensselaer Polytechnic Institute, 2021.

"Investigating the Inductive Biases in Graph Neural Networks." Invited talk at HEALTH[at]SCALE, 2021.

"Investigating the Inductive Biases in Graph Neural Networks." Invited talk at Google, 2021.

"Towards More Practical Adversarial Attacks on Graph Neural Networks." Invited talk at Graph Reading Group, Mila-Quebec AI institute, 2020.

"A Flexible Generative Framework for Graph-based Semi-supervised Learning." Invited talk at Statistics Student Seminar, University of Michigan, 2019.