Jingrui He

Associate Professor

PhD, Machine Learning, Carnegie Mellon University

Other professional appointments

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)

Research focus

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.

Honors and Awards

  • 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

Biography

Jingrui He is an associate professor in the School of Information Sciences at the University of Illinois Urbana-Champaign. She received her PhD from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in security, social network analysis, healthcare, and manufacturing processes. Dr. He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT Award, three-time recipient of the IBM Faculty Award in 2018, 2015, and 2014, and was selected as IJCAI 2017 Early Career Spotlight. Dr. He has more than 100 publications at major conferences (e.g., IJCAI, AAAI, KDD, ICML, NeurIPS) and journals (e.g., TKDE, TKDD, DMKD), 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.

Courses currently teaching

Office hours

By appointment, please contact professor

Publications & Papers

Z. Wu, and J. He. Fairness-aware Model-agnostic Positive and Unlabeled Learning. ACM FAccT 2022 [Distinguished Paper Award] (acceptance rate: 25%)
D. Fu, L. Fang, R. Maciejewski, V. Tovik, and J. He. Meta-Learned Metrics over Multi-Evolution Temporal Graphs. KDD 2022 (acceptance rate: 14.99%)
L. Zheng, J. Xiong, Y. Zhu, and J. He. Contrastive Learning with Complex Heterogeneity. KDD 2022 (acceptance rate: 14.99%)
T. Wei, and J. He. Comprehensive Fair Meta-learned Recommender System. KDD 2022 (acceptance rate: 14.99%)
Y. Qi, Y. Ban, and J. He. Neural Bandit with Arm Group Graph. KDD 2022 (acceptance rate: 14.99%)
J. Wu, and J. He. Domain Adaptation with Dynamic Open-Set Targets. KDD 2022 (acceptance rate: 14.99%)
Y. Ban, Y. Yan, A. Banerjee, and J. He. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022 (spotlight, acceptance rate: 5.2%) 

Z. Wu, and J. He. Fairness-aware Model-agnostic Positive and Unlabeled Learning. ACM FAccT 2022 [Distinguished Paper Award] (acceptance rate: 25%)

D. Fu, L. Fang, R. Maciejewski, V. Tovik, and J. He. Meta-Learned Metrics over Multi-Evolution Temporal Graphs. KDD 2022 (acceptance rate: 14.99%)

L. Zheng, J. Xiong, Y. Zhu, and J. He. Contrastive Learning with Complex Heterogeneity. KDD 2022 (acceptance rate: 14.99%)

T. Wei, and J. He. Comprehensive Fair Meta-learned Recommender System. KDD 2022 (acceptance rate: 14.99%)

Y. Qi, Y. Ban, and J. He. Neural Bandit with Arm Group Graph. KDD 2022 (acceptance rate: 14.99%)

J. Wu, and J. He. Domain Adaptation with Dynamic Open-Set Targets. KDD 2022 (acceptance rate: 14.99%)

Y. Ban, Y. Yan, A. Banerjee, and J. He. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022 (spotlight, acceptance rate: 5.2%)

Y. Zhou, J. Xu, J. Wu, Z.T. Nasrabadi, E. Korpeoglu, K. Achan, and J. He. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD 2021 (acceptance rate: 15.4%)

J. Wu, and J. He. Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD 2021 (acceptance rate: 15.4%)

Y. Ban, J. He, and C.B. Cook. Multi-facet Contextual Bandits: A Neural Network Perspective. KDD 2021 (acceptance rate: 15.4%)

 

Presentations

 

“Towards Understanding Users’ Behaviors in Multi-Armed Bandits” Keynote talk at the 6th workshop on Automation in Machine Learning, 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.

"Exploring Rare Categories on Graphs: Local vs. Global" Invited talk at the 1st workshop on Artificial Intelligence for Anomalies and Novelties, 2021.

"Exploring Rare Categories on Graphs: Local vs. Global" Keynote talk at the 4th workshop on Graph Techniques for Adversarial Activity Analytics, 2020.

"It's All About Balance: A Career in CS vs. Life" Invited talk at WCS Explore CS Series, 2020.

"Taming Data Heterogeneity: Towards a Unified Framework." Invited talk at the University of Illinois, 2018.

"Taming Data Heterogeneity: Algorithms and Applications." Invited talk at Stony Brook University, 2018.

"Seeking Common Ground: Learning from Data Heterogeneity." Invited talk at Dartmouth College and American Express, 2018.