Jingrui He

• Faculty Affiliate, NSF-Simons National Institute for Theory and Mathematics in Biology
• Research Affiliate, Mayo Clinic Arizona
• Faculty Affiliate, Department of Computer Science, University of Illinois
• Faculty Affiliate, National Center for Supercomputing Applications (NCSA)
• Faculty Affiliate, Illinois Informatics
• Faculty Affiliate, Center for Digital Agriculture (CDA)

Designing, building, and testing a suite of automated and semi-automated methods to explore, understand, characterize, and predict real-world data by means of statistical machine learning.

• ICCV MMRAgI Workshop Outstanding Paper Award, 2025
• NeurIPS Top Reviewer, 2025
• Amazon Research Award, 2025
• ACM Distinguished Member, 2023
• AAAI Senior Member, 2023
• FAccT Distinguished Paper Award, 2022
• Teachers Ranked as Excellent by Their Students, 2021
• Outstanding Academic Title, 2020
• ICML Top Reviewer, 2020
• IEEE Senior Member, 2020
• IBM Faculty Award, 2018
• 24th Capitol Hill Science Exhibition, 2018
• IJCAI Early Career Spotlight, 2017
• NSF CAREER award, 2016
• Springer Knowledge and Information Systems (KAIS) on "Best of ICDM 2016"
• IBM Faculty Award, 2015
• IBM Faculty Award, 2014
• Statistical Analysis and Data Mining on “Best of SDM 2010”, 2010
• Frontiers of Computer Science on “Best of ICDM 2010”, 2010
• IEEE ICDM Contest on Traffic Prediction Runner-up for Task 2 (Jams) and Task 3 (GPS), 2010
• IBM Fellowship, 2009
• IBM Fellowship, 2008

Jingrui He is a professor in the School of Information Sciences, University of Illinois Urbana-Champaign. She received her PhD from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning, with applications in sustainability, agriculture, social network analysis, healthcare, and finance. 

Professor He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT Award, the 2025 Amazon Research Award, three times recipient of the IBM Faculty Award in 2018, 2015 and 2014 respectively, and was selected as IJCAI 2017 Early Career Spotlight. She has more than 200 publications at major conferences (e.g., ICML, NeurIPS, ICLR, KDD) and journals (e.g., TMLR, TKDD, JMLR, JAIR), and is the author of two books. Her papers have received the Distinguished Paper Award at FAccT 2022, the Outstanding Paper Award at ICCV 2025 MMRAgI Workshop, as well as Bests of the Conference at ICDM 2016, ICDM 2010, and SDM 2010. She is a Distinguished Member of ACM, a Senior Member of AAAI and IEEE. She is also the Program Co-chair of IEEE BigData 2023.

• Fall 2025 - Data Mining - IS577AC
• Fall 2025 - Independent Study - IS589JRH
• Spring 2026 - Independent Study - IS589JRH

By appointment, please contact professor

J. Zou, Y. Ban, Z. Li, Y. Qi, R. Qiu, L. Yang, and J. He. Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning. NeurIPS 2025 (spotlight)

J. Zou, L. Yang, J. Gu, J. Qiu, K. Shen, J. He, and M. Wang. Trajectory-aware PRMs for Long CoT Reasoning. NeurIPS 2025

W. Bao, R. Deng, and J. He. Mint: A Simple Test-Time Adaptation of Vision-Language Models against Common Corruptions. NeurIPS 2025

Z. Liu, Z. Li, Z. Yang, T. Wei, J. Kang, Y. Zhu, H. Hamann, J. He, and H. Tong. CLIMB: Class-imbalanced Learning Benchmark on Tabular Data. NeurIPS 2025

X. Ning, D. Fu, T. Wei, W. Xu, and J. He. Graph4MM: Weaving Multimodal Learning with Structural Information. ICML 2025

K. Tieu, D. Fu, Z. Li, R. Maciejewski, and J. He. Learnable Spatial-Temporal Positional Encoding for Link Prediction. ICML 2025

Z. Liu, Z. Yang, X. Lin, R. Qiu, T. Wei, Y. Zhu, H. Hamann, J. He, and H. Tong. Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting. ICML 2025

W. Bao, Z. Zeng, Z. Liu, H. Tong, and J. He. AdaRC: Mitigating Graph Structure Shifts during Test-Time. ICLR 2025

X. He, D. Fu, H. Tong, R. Maciejewski, and J. He. Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency. ICLR 2025 (spotlight)

W. Bao, R. Deng, R. Qiu, T. Wei, H. Tong, and J. He. Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning. ICCV 2025

• “Exploitation vs. Exploration in Sequential Decision-Making”
  • Keynote talk at the workshop on Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation in conjunction with ICLR, 2025
• “Harnessing Distribution Shifts in Graphs”
  • Invited talk at Yale University CS Colloquium Series, 2025
  • Keynote talk at the workshop on Machine Learning on Graphs in the Era of Generative Artificial Intelligence in conjunction with KDD, 2025
• “Towards Multimodal Understanding on Rich Data: IID vs. Non-IID”
  • Keynote talk at the workshop on CRAG-MM: Comprehensive RAG Benchmark for Multi-modal, Multi-turn Challenge in conjunction with KDD, 2025
• “Multifaceted Robustness in Transfer Learning”
  • Invited talk at the University of North Dakota Distinguished Webinar, 2024
  • Invited talk at the Vanderbilt Machine Learning Seminar, 2024
• “Federated Learning with Data Heterogeneity”
  • Invited talk at INFORMS, 2024
• “Graph Transfer Learning”
  • Invited talk at the workshop on GNNs for the Sciences: from Theory to Practice, 2024
• “Towards Understanding Users’ Behaviors in Multi-Armed Bandits"
  • Keynote talk at the 6th workshop on Automation in Machine Learning in conjunction with KDD, 2022
  • Invited talk at the School of Information, University of Michigan, 2022
  • Invited talk at the Department of Computer Science, Brandeis University, 2022
• “Towards Understanding the Users in Recommender Systems”
  • Keynote talk at Meta Advanced Algorithm Ivory Tower Symposium, 2022
• “Towards Understanding Rare Categories on Graphs”
  • Keynote talk at the workshop on Machine Learning on Graphs in conjunction with WSDM, 2022