Jingrui He

• 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.

• 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 at 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 security, agriculture, social network analysis, healthcare, and finance.

Jingrui He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT 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 170 publications at major conferences (e.g., ICML, NeurIPS, ICLR, KDD) and journals (e.g., TKDE, TKDD, JMLR), and is the author of two books. Her papers have received the Distinguished Paper Award at FAccT 2022, 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 2024 - Data Mining - IS577AC
• Fall 2024 - Independent Study - IS589JRH
• Spring 2025 - Independent Study - IS589JRH

By appointment, please contact professor

Y. Ban, I. Agarwal, Z. Wu, Y. Zhu, K. Weldemariam, H. Tong, and J. He. Neural Active Learning Beyond Bandits. ICLR 2024 

T. Wei, B. Jin, R. Li, H. Zeng, Z. Wang, J. Sun, Q. Yin, H. Lu, S. Wang, J. He, and X. Tang. Towards Universal Multi-Modal Personalization: A Language Model Empowered Generative Paradigm. ICLR 2024

R. Deb, Y. Ban, S. Zuo, J. He, and A. Banerjee. Contextual Bandits with Online Neural Regression. ICLR 2024

D. Fu, Z. Hua, Y. Xie, J. Fang, S. Zhang, K. Sancak, H. Wu, A. Malevich, J. He, and B. Long. VCR-Graphormer: A Mini-Batch Graph Transformer via Virtual Connections. ICLR 2024

Z. Liu, R. Qiu, Z. Zeng, H. Yoo, D. Zhou, Z. Xu, Y. Zhu, K. Weldemariam, J. He, and H. Tong. Class-Imbalanced Graph Learning without Class Rebalancing. ICML 2024

Z. Zeng, R. Qiu, Z. Xu, Z. Liu, Y. Yan, T. Wei, L. Ying, J. He, and H. Tong. Graph Mixup on Approximate Gromov–Wasserstein Geodesics. ICML 2024 

J. Wu, E.A. Ainsworth, A. Leakey, H. Wang, and J. He. Graph-Structured Gaussian Processes for Transferable Graph Learning. NeurIPS 2023 

Y. Qi, Y. Ban, T. Wei, J. Zou, H. Yao, and J. He. Meta-Learning with Neural Bandit Scheduler. NeurIPS 2023 

W. Bao, T. Wei, H. Wang, and J. He. Adaptive Test-Time Personalization for Federated Learning. NeurIPS 2023 

W. Bao, H. Wang, J. Wu, and J. He. Optimizing the Collaboration Structure in Cross-silo Federated Learning. ICML 2023

• “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, 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, 2022
• “Exploring Rare Categories on Graphs: Local vs. Global”
  • Keynote talk at the workshop on Deep Learning on Graphs: Method and Applications, 2021
  • Invited talk at the 1st workshop on Artificial Intelligence for Anomalies and Novelties, 2021
  • Keynote talk at the 4th workshop on Graph Techniques for Adversarial Activity Analytics, 2020
• “Taming Data Heterogeneity: Algorithms and Applications”
  • Invited talk at Stony Brook University, 2018
• “Seeking Common Ground: Learning from Data Heterogeneity”. 
  • Invited talk at American Express, 2018