Han Zhao
Assistant Professor
Studies trustworthy machine learning, including fairness, domain adaptation and alignment of large language models.
Han Zhao's lab at UIUC works on trustworthy & safe ML, interpretability, evaluation & safety of LMs and large language models. 61 papers since 2023, 50 at top venues, mostly NeurIPS and TMLR. 6 PhD students and 1 master's student. 14 alumni, most often to Google and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 82%
- PhD students
- 6
- MS students
- 1
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 8 · collecting
- 15 papers2025 · T1 12
- 24 papers2024 · T1 19
- 13 papers2023 · T1 11
T1 venuesOtherVenues: NeurIPS, TMLR, ICML, ICLR, AISTATS
Research focuspaper titles that use each topic's words
- Large language models6 papers
- Trustworthy & safe ML4 papers
- domain adaptation4 papers
- Interpretability, evaluation & safety of LMs1 paper
- studies trustworthy0 papers
Paper list
From the lab homepage.
20269 papers
- ICML (T1)ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
- TMLR (T1)Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
- ICML (T1)Convex Dataset Valuation for Post-Training
- TMLR (T1)Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
- Machine LearningNeural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning
- TMLR (T1)Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Optimization
- AISTATS (T1)Structural Alignment Improves Graph Test-Time Adaptation
- TMLR (T1)Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic
- TMLR (T1)Weakly-Supervised Disentangled Representation Learning via Filter-Based Adaptive Swapping
202515 papers
- NeurIPS (T1)A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
- ICLR (T1)Accelerating Neural ODEs: A Variational Formulation-based Approach
- In ACM Transactions on Intelligent Systems and Technology (TIST ).An Improved Autoregressive Evaluation Paradigm for Large Language Models
- NeurIPS (T1)Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
- NeurIPS (T1)GraSS: Scalable Influence Function with Sparse Gradient Compression
- ICLR (T1)Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport
- TMLR (T1)Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic
- NeurIPS (T1)MergeBench: A Benchmark for Merging Domain-Specialized LLMs
- In Proceedings of the Association for Computational Linguistics: EMNLPMiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
- UAI (T1)Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization
- ICML (T1)Multiobjective Distribution Matching
- ACL (T1)Scaling Laws for Multilingual Language Models
202424 papers
- TMLR (T1)A General-Purpose Multi-Modal OOD Detection Framework
- JBIA Survey of Recent Methods for Addressing AI Fairness and Bias in Biomedicine
- EntropyAn Empirical Study of Self-Supervised Learning with Wasserstein Distance
- ACL (T1)Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
- ICML (T1)Differentially Private Post-Processing for Fair Regression
- JMLR (T1)Efficient Modality Selection in Multimodal Learning
- TMLR (T1)Enhancing Compositional Generalization via Compositional Feature Alignment
- AI MagazineFair and Optimal Prediction via Post-Processing
- AISTATS (T1)Fast 1-Wasserstein distance approximations using greedy strategies
- NeurIPS (T1)FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
- ICLR (T1)FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
- JMLR (T1)Gradual Domain Adaptation: Theory and Algorithms
202313 papers
- JMLR (T1)Adaptation Augmented Model-based Policy Optimization
- ALTAdaptive Power Method: Eigenvector Estimation from Sampled Data
- TMLR (T1)Costs and Benefits of Fair Regression
- NeurIPS (T1)Efficient Learning of Linear Graph Neural Networks via Node Subsampling
- ICML (T1)Fair and Optimal Classification via Post-Processing
- AAMAS (T1)FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation
- NeurIPS (T1)Learning List-Level Domain-Invariant Representations for Ranking
- ICLR (T1)Learning Structured Representations by Embedding Class Hierarchy
- NeurIPS (T1)Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
- ICML (T1)Structural Re-weighting Improves Graph Domain Adaptation
- ECML-PKDDTrain Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
- CVPR (T1)Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning
Where alumni went14 listed, 55% to industry
Industry 6Academia 5
Most often to
- Google DeepMind
- Microsoft
Also to
- MIT
- NYU
- Stanford 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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