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

  • 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 an associate professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. She received her PhD in machine learning 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 social network analysis, healthcare, and manufacturing processes. She is the recipient of the 2016 NSF CAREER Award and a three-time recipient of the IBM Faculty Award, in 2018, 2015 and 2014 respectively, and was selected for an IJCAI 2017 Early Career Spotlight. He has published more than 90 refereed articles, and is the author of the book, Analysis of Rare Categories (Springer-Verlag, 2011). Her papers have been selected as "Best of the Conference" by ICDM 2016, ICDM 2010, and SDM 2010. She has served on the senior program committee/program committee for Knowledge Discovery and Data Mining (KDD), International Joint Conference on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), SIAM International Conference on Data Mining (SDM), and International Conference on Machine Learning (ICML). 

Courses currently teaching

Office hours

By appointment, please contact professor

Publications & Papers

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%)

L. Zheng, Y. Cheng, H. Yang, N. Cao, and J. He. Deep Co-Attention Network for Multi-View Subspace Learning. WWW 2021 (acceptance rate: 20.6%)

Y. Ban, and J. He. Local Clustering in Contextual Multi-Armed Bandits. WWW 2021 (acceptance rate: 20.6%)

H. Wang, C. Zhou, C. Yang, H. Yang, and J. He. Controllable Gradient Item Retrieval. WWW 2021 (acceptance rate: 20.6%)

D. Fu, and J. He. SDG: A Simplified and Dynamic Graph Neural Network. SIGIR 2021 (acceptance rate: 27.6%)

J. Li, L. Zheng, Y. Zhou, and J. He. Outlier Impact Characterization for Time Series Data. AAAI 2021 (acceptance rate: 21%)D. Zhou, L. Zheng, J. Han, and J. He: A Data Driven Graph Generative Model for Temporal Interaction Networks. KDD 2020 (acceptance rate: 17%)

J. Kang, J. He, R. Maciejewski and H. Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020 (acceptance rate: 17%)

Y. Ban, and J. He: Generic Outlier Detection in Multi-Armed Bandit. KDD 2020 (acceptance rate: 17%)

D. Fu, D. Zhou, and J. He: Local Motif Clustering on Time-Evolving Graphs. KDD 2020 (acceptance rate: 17%)

Y. Zhou, A. Nelakurthi, R. Maciejewski, W. Fan, and J. He. Crowd Teaching with Imperfect Labels. WWW 2020 (acceptance rate: 19%)

D. Zhou, L. Zheng, Y. Zhu, J. Li, and J. He. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. WWW 2020 (acceptance rate: 19%)

Z. Liu, D. Zhou, Y. Zhu, J. Gu, and J. He. Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk. AAAI 2020 (acceptance rate: 20.6%)

P. Yang, Q. Tan, H. Tong, and J. He. Task-Adversarial Co-Generative Nets. KDD 2019 (acceptance rate of research track: 14%)

J. Wu, J. He, and J. Xu. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019 (acceptance rate of research track: 14%)

Y. Zhou, A. Nelakurthi, and J. He. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. KDD 2018 (acceptance rate of research track long presentation: 10.9%)

D. Zhou, J. He, H. Yang, and W. Fan. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. KDD 2018 (acceptance rate of research track short presentation: 18.4%)

J. Li, J. He, and Y. Zhu. E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. KDD 2018 (acceptance rate of applied data science track oral presentation: 8.1%)

D. Zhou, S. Zhang, M. Yildirim, S. Alcorn, H. Tong, H. Davulcu and J. He. A Local Algorithm for Structure-Preserving Graph Cut. KDD 2017: 655-664 [Student Travel Award] (acceptance rate of research track: 18.9%)

H. Yang, Y. Zhu and J. He. Local Algorithm for User Action Prediction Towards Display Ads. KDD 2017: 2091-2099 (acceptance rate of applied data science track: 21.5%)

P. Yang, Q. Tan and J. He. Multi-task Function-on-function Regression with Co-grouping Structured Sparsity. KDD 2017: 1255-1264 (acceptance rate of research track: 18.9%)

D. Zhang, J. He, L. Si, and R. Lawrence. MILEAGE: Multiple Instance LEArning with Global Embedding. ICML 2013: 82-90 (acceptance rate: 24%)

J. He, H. Tong, Q. Mei and B. Szymanski. GenDeR: A Generic Diversified Ranking Algorithm. NIPS 2012: 1151-1159 (acceptance rate: 25%)


"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.

"Learning from Data Heterogeneity: Algorithms and Applications." Invited talk at IJCAI 2017 Early Career Spotlight, Fudan University, Nanjing University, and Alibaba, 2017.

"Rare Category Analysis for Rich Data." Keynote talk at IJCAI 2017 Workshop on AI Applications in E-Commerce.

"Detecting Complex Rare Categories in Big Data: Theory and Applications." Invited talk at Banner Alzheimer's Institute, 2015.