Jiaqi Ma | 马家祺
School of Information Sciences
University of Illinois Urbana-Champaign
I am an Assistant Professor in the School of Information Sciences, University of Illinois Urbana-Champaign. Prior to UIUC, I was a Postdoctoral Researcher at Harvard University. I received my Ph.D. degree from University of Michigan and a B.Eng. degree from Tsinghua University.
I’m generally interested in understanding and improving machine learning (ML) for complex real-world data (e.g., graphs, rankings, partially observed data), under different contexts (e.g., distribution shift), from various aspects (e.g., accuracy, efficiency, robustness, fairness). I’m particularly interested in scenarios where humans are involved. Some “buzzwords” relevant to my existing research include trustworthy ML, graph ML, and recommender systems. I’m also dipping into explainable ML and NLP.
For students who want to work with me, please see here for more details.
- Two papers accepted by NeurIPS 2023!
- I’m serving as an Area Chair for ICLR 2024!
- We are organizing the 1st Workshop on Regulatable Machine Learning in conjunction with NeurIPS 2023!
- I’m serving as an Area Chair for CPAL 2024 and a Vice Program Chair for IEEE BigData 2023!
- We are organizing the 3rd Workshop on Graph Learning Benchmarks (GLB) in conjunction with KDD 2023!
- One paper on algorithmic recourse and unlearning accepted by ICML 2023!
- One paper on active learning for GNNs accepted by TMLR 2023!
- One paper on evaluating chemical space coverage metrics accepted by ICLR 2023!
- A Metadata-Driven Approach to Understand Graph Neural Networks.
Ting Wei Li, Qiaozhu Mei, Jiaqi Ma.
- Post Hoc Explanations of Language Models Can Improve Language Models.
Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju.
- Towards Bridging the Gaps Between the Right to Explanation and the Right to be Forgotten.
Satyapriya Krishna*, Jiaqi Ma*, Himabindu Lakkaraju.
- 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.
- 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).
- Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling.
Jiaqi Ma*, Xingjian Zhang*, Qiaozhu Mei.
- Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
- Subgroup Generalization and Fairness of Graph Neural Networks.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
NeurIPS 2021 (Spotlight, top 3%).
- 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.
- CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks.
Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei.
- Towards More Practical Adversarial Attacks on Graph Neural Networks.
Jiaqi Ma*, Shuangrui Ding*, Qiaozhu Mei.
- 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).
- A Flexible Generative Framework for Graph-based Semi-supervised Learning.
Jiaqi Ma*, Weijing Tang*, Ji Zhu, Qiaozhu Mei.
- SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning.
Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, Ed H. Chi.
- 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).
- DeepCas: An End-to-End Predictor of Information Cascades.
Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei.
- Partition-Based Active Learning for Graph Neural Networks.
Jiaqi Ma*, Ziqiao Ma*, Joyce Chai, Qiaozhu Mei.
- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks.
Weijing Tang*, Jiaqi Ma*, Qiaozhu Mei, Ji Zhu.
- Semi-Supervised Joint Learning for Longitudinal Clinical Events Classiﬁcation Using Neural Network Models.
Weijing Tang, Jiaqi Ma, Akbar K. Waljee, Ji Zhu.
(* Equal Contribution)
- Instructor, IS 327, Fall 2023, University of Illinois Urbana-Champaign.
Concepts of Machine Learning.
- Co-Instructor, COMPSCI 282BR, Spring 2023, Harvard University.
Explainable AI: From Simple Rules to Complex Generative Models.
Pronunciation of my first name: Jia-Chi.