Jingrui He
Professor
Studies heterogeneous machine learning, active learning, neural bandits and self-supervised learning, with applications in security, healthcare and agriculture.
Jingrui He's lab at UIUC works on reinforcement learning and generative & foundation models. 141 papers since 2023, 114 at top venues, mostly KDD and ICLR. 24 PhD students and 2 master's students. 41 alumni, most often to Amazon and Meta. Applications close .
At a glance
- papers, 2023–2026
- 37/ year
- at T1 venues
- 81%
- PhD students
- 24
- MS students
- 2
- alumni listed
- 41
Papers by yearfrom the lab homepage, 2023–2026
- 34 papers2026 · T1 29 · collecting
- 37 papers2025 · T1 34
- 40 papers2024 · T1 31
- 30 papers2023 · T1 20
T1 venuesOtherVenues: KDD, ICLR, ICML, NeurIPS, WWW
Research focuspaper titles that use each topic's words
- Reinforcement learning33 papers
- Generative & foundation models15 papers
- bandits5 papers
- active learning1 paper
- self-supervised learning1 paper
Paper list
From the lab homepage.
202634 papers
- ACL (T1)AdaFuse: Adaptive Ensemble Decoding for Large Language Models
- ICML (T1)Auto-regressive In-context Demonstration Selection
- ICML (T1)AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
- ICML (T1)AvAtar: Learning to Align via Active Optimal Transport
- ICML (T1)Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
- ICLR (T1)Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
- Findings of ACLDo VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models
- KDD (T1)DuET: Dual-View Tensor-to-Topology Spectral Adapter for Enhancing Sparse Tensor Factorization
- WWW (T1)FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation
- KDD (T1)Generalizable Multi-Pass Training of Ads Recommendation Models with Foundation Model Guidance
- KDD (T1)Graph Diffusion History Reconstruction via Feasibility-Aware Markov Chain Monte Carlo Estimation
- ICLR (T1)Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
202537 papers
- KDD (T1)APEX2: Adaptive and Extreme Summarization for Personalized Knowledge Graphs
- ICLR (T1)Ask, and it shall be given: On the Turing completeness of prompting
- ICML (T1)Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
- ACL (T1)Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
- CIKM (T1)ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
- NeurIPS (T1)CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
- WWW (T1)Cluster Aware Graph Anomaly Detection
- KDD (T1)Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization
- Tutorial. The ACM International Conferenceon Information and Knowledge Management, Seoul, South Korea (CIKMContinual Recommender Systems
- SIGIR (T1)Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems
- CIKM (T1)Few-Shot Knowledge Graph Completion via Transfer from Similar Tasks
- CIKM (T1)Fine-grained Graph Rationalization
202440 papers
- KDD (T1)AIM: Attributing, Interpreting, Mitigating Data Unfairness
- CIKM (T1)Automated Contrastive Learning Strategy Search for Time Series
- Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems , Vancouver, CanadaBackTime: Backdoor Attacks on Multivariate Time Series Forecasting
- AISTATS (T1)BOBA: Byzantine-Robust Federated Learning with Label Skewness
- ICML (T1)Class-Imbalanced Graph Learning without Class Rebalancing
- WWW (T1)Co-clustering for Federated Recommender System
- Findings of ACLConversational Question Answering with Language Models Generated Reformulations over Knowledge Graph
- ICLR (T1)Deceptive Fairness Attacks on Graphs via Meta Learning
- NeurIPS (T1)Discrete-state Continuous-time Diffusion for Graph Generation
- KDD (T1)Distributional Network of Networks for Modeling Data Heterogeneity
- WWW (T1)Ensuring User-side Fairness in Dynamic Recommender Systems
- ICDE (T1)Fairgen: Towards Fair Graph Generation
202330 papers
- IEEE Transactions on Knowledge and Data Engineering (TKDEA Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation
- NeurIPS (T1)Adaptive Test-Time Personalization for Federated Learning
- IEEE Transactions on Knowledge and Data Engineering (TKDEAdversarial Attacks on Multi-Network Mining: Problem Definition and Fast Solutions
- The Second Learning on Graphs Conference (LoGBeMap: Balanced Message Passing for Fair Graph Neural Network
- WWW (T1)Everything Evolves in Personalized PageRank
- WWW (T1)Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs
- NeurIPS (T1)From Trainable Negative Depth to Edge Heterophily in Graphs
- ICML (T1)Generative Graph Dictionary Learning
- CIKM (T1)Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
- KDD (T1)Graph Neural Bandits
- NeurIPS (T1)Graph-Structured Gaussian Processes for Transferable Graph Learning
- KDD (T1)Kernel Ridge Regression-Based Graph Dataset Distillation
Where alumni went41 listed, 52% to industry
Industry 16Academia 15
Most often to
- Amazon
- Meta
Also to
- UIUC
- Carnegie Mellon University
- Chung-Ang University
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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