Jiaqi Ma | 马家祺

Assistant Professor
School of Information Sciences
Siebel School of Computing and Data Science (Affiliation)
University of Illinois Urbana-Champaign

Contact:
Outlook: jiaqima AT illinois DOT edu
Gmail: jiaqima.mle AT gmail DOT com
[Google Scholar | GitHub | Bluesky | Twitter]

About Me

I am an Assistant Professor at the University of Illinois Urbana-Champaign (UIUC). Prior to UIUC, I was a Postdoctoral Researcher at Harvard University. I received my Ph.D. from University of Michigan and a B.Eng. from Tsinghua University.

I’m interested in the broad area of trustworthy artificial intelligence (AI). Recently, my research focuses on data-centric problems, including data attribution, machine unlearning, training data curation, and copyright issues of generative AI.

For students who want to work with me, please see here for more details.

News

• Giving a talk about our work on data attribution at the AI Seminar at UMich in May 2025!
• Giving a talk at the New Faculty Highlight session at AAAI 2025!
• I’m serving as an Area Chair for ICML 2025!
• Giving a talk about our work on data attribution at RIKEN AIP in Tokyo in Jan 2025!
• Giving a talk about our work on data attribution at the VASC Seminar at CMU in Dec 2024!
• Giving a talk about our work on data attribution at the IDEAL Institute in Chicago in Nov 2024!
• Two papers accepted by NeurIPS 2024 (with one spotlight at the D&B track)!
• I’m serving as an Area Chair for AISTATS 2025!
• Giving a talk about our work on data attribution at the Siebel School Colloquium at UIUC in Sep 2024!
• We are organizing the 2nd Workshop on Regulatable Machine Learning in conjunction with NeurIPS 2024!
• Our work on Computational Copyright received a best paper award from the DPFM Workshop at ICLR 2024!
• One paper accepted by AISTATS 2024!
• I’m serving as an Area Chair for ICML 2024!

Selected Papers

Preprints

• DCA-Bench: A Benchmark for Dataset Curation Agents.
  Benhao Huang, Yingzhuo Yu, Jin Huang, Xingjian Zhang, Jiaqi Ma.
  [ArXiv]
• Efficient Ensembles Improve Training Data Attribution.
  Junwei Deng*, Ting Wei Li*, Shichang Zhang, Jiaqi Ma.
  [ArXiv]
• Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View.
  Yiwen Tu*, Pingbang Hu*, Jiaqi Ma.
  [ArXiv]
• Computational Copyright: Towards A Royalty Model for AI Music Generation Platforms.
  Junwei Deng, Jiaqi Ma.
  [ArXiv]

Conference Publications

• A Versatile Influence Function for Data Attribution with Non-Decomposable Loss.
  Junwei Deng, Weijing Tang, Jiaqi Ma.
  ICML 2025.
  [ArXiv]
• Adversarial Attacks on Data Attribution.
  Xinhe Wang, Pingbang Hu, Junwei Deng, Jiaqi Ma.
  ICLR 2025.
  [ArXiv]
• dattri: A Library for Efficient Data Attribution.
  Junwei Deng*, Ting Wei Li*, Shiyuan Zhang, Shixuan Liu, Yijun Pan, Hao Huang, Xinhe Wang, Pingbang Hu, Xingjian Zhang, Jiaqi Ma.
  NeurIPS 2024 (Datasets and Benchmarks Track, Spotlight).
  [ArXiv][GitHub]
• Most Influential Subset Selection: Challenges, Promises, and Beyond.
  Yuzheng Hu, Pingbang Hu, Han Zhao, Jiaqi Ma.
  NeurIPS 2024.
  [ArXiv]
• Fair Machine Unlearning: Data Removal while Mitigating Disparities.
  Alex Oesterling, Jiaqi Ma, Flavio P. Calmon, Himabindu Lakkaraju.
  AISTATS 2024.
  [ArXiv]
• A Metadata-Driven Approach to Understand Graph Neural Networks.
  Ting Wei Li, Qiaozhu Mei, Jiaqi Ma.
  NeurIPS 2023.
  [ArXiv]
• Post Hoc Explanations of Language Models Can Improve Language Models.
  Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju.
  NeurIPS 2023.
  [ArXiv]
• Towards Bridging the Gaps Between the Right to Explanation and the Right to be Forgotten.
  Satyapriya Krishna*, Jiaqi Ma*, Himabindu Lakkaraju.
  ICML 2023.
  [OpenReview]
• How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules.
  Yutong Xie, Ziqiao Xu, Jiaqi Ma, Qiaozhu Mei.
  ICLR 2023.
  [OpenReview]
• Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks.
  Jiaqi Ma*, Xingjian Zhang*, Hezheng Fan, Jin Huang, Tianyue Li, Ting Wei Li, Yiwen Tu, Chenshu Zhu, Qiaozhu Mei.
  LOG 2022 (Oral).
  [OpenReview][Codebase][Documentation]
• Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling.
  Jiaqi Ma*, Xingjian Zhang*, Qiaozhu Mei.
  WSDM 2022.
  [ArXiv][Code]
• Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem.
  Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
  WSDM 2022.
  [ArXiv][Code]
• Subgroup Generalization and Fairness of Graph Neural Networks.
  Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
  NeurIPS 2021 (Spotlight, top 3%).
  [ArXiv][Code]
• Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model.
  Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei.
  AISTATS 2021.
  [ArXiv][SlidesLive]
• CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks.
  Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei.
  ICLR 2021.
  [ArXiv][OpenReview][Code][SlidesLive]
• Towards More Practical Adversarial Attacks on Graph Neural Networks.
  Jiaqi Ma*, Shuangrui Ding*, Qiaozhu Mei.
  NeurIPS 2020.
  [ArXiv][Code][SlidesLive]
• Off-policy Learning in Two-stage Recommender Systems.
  Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi.
  TheWebConf (WWW) 2020 (with oral presentation).
  [Proceedings][Code]
• A Flexible Generative Framework for Graph-based Semi-supervised Learning.
  Jiaqi Ma*, Weijing Tang*, Ji Zhu, Qiaozhu Mei.
  NeurIPS 2019.
  [Proceedings][Code]
• Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.
  Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed H. Chi.
  KDD 2018 (with oral presentation).
  [Proceedings][Video][Presentation]

Journal Publications

• Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
  Jin Huang, Xingjian Zhang, Qiaozhu Mei, Jiaqi Ma.
  TMLR 2023.
  [ArXiv]
• Partition-Based Active Learning for Graph Neural Networks.
  Jiaqi Ma*, Ziqiao Ma*, Joyce Chai, Qiaozhu Mei.
  TMLR 2023.
  [ArXiv]
• SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks.
  Weijing Tang*, Jiaqi Ma*, Qiaozhu Mei, Ji Zhu.
  JMLR 2022.
  [ArXiv][Code]

(* Equal Contribution)

(Note: I publish under the name Jiaqi W. Ma, starting in Sep 2024.)

PhD Students

• Junwei Deng
• Pingbang Hu
• Shixuan Liu

Teaching

• Instructor, IS 527, SP24/SP25, University of Illinois Urbana-Champaign.
  Network Analysis.
• Instructor, IS 327, FA23/SP25, University of Illinois Urbana-Champaign.
  Concepts of Machine Learning.
• Co-Instructor, COMPSCI 282BR, SP23, Harvard University.
  Explainable AI: From Simple Rules to Complex Generative Models.

Misc

Pronunciation of my first name: Jia-Chi.