Honglak Lee
Professor, EECS – Computer Science and Engineering
Studies deep learning and representation learning, with applications to computer vision, multimodal models and reinforcement learning.
Honglak Lee's lab at Michigan works on reinforcement learning, generative & foundation models and multimodal & grounded language. 67 papers since 2023, 47 at top venues, mostly NeurIPS and AAAI. 7 PhD students. 41 alumni, most often to Google and Amazon. Applications close .
At a glance
- papers, 2023–2026
- 17/ year
- at T1 venues
- 70%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 41
Papers by yearfrom DBLP, 2023–2026
- 9 papers2026 · T1 5 · collecting
- 15 papers2025 · T1 12
- 17 papers2024 · T1 9
- 26 papers2023 · T1 21
T1 venuesOtherVenues: NeurIPS, AAAI, EMNLP, CVPR, ICLR
Research focuspaper titles that use each topic's words
- Multimodal & grounded language17 papers
- Generative & foundation models15 papers
- Reinforcement learning12 papers
- multimodal models0 papers
- studies deep0 papers
Paper list
From DBLP.
20269 papers
- CVPR (T1)AnthroTAP: Learning Point Tracking with Real-World Motion (opens in a new tab)
- EACLBeyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (opens in a new tab)
- CVPR (T1)Enhancing Mixture-of-Experts Specialization via Cluster-Aware Upcycling (opens in a new tab)
- Findings of EACLIRPO: Implicit Policy Regularized Preference Optimization (opens in a new tab)
- CVPR (T1)Learning complete and explainable visual representations from itemized text supervision (opens in a new tab)
- Trans. Mach. Learn. Res.Process Reward Models That Think (opens in a new tab)
- ICLR (T1)SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety (opens in a new tab)
- ECCV (T1)Spanning Tree Autoregressive Visual Generation (opens in a new tab)
- Trans. Mach. Learn. Res.Towards Scalable Language-Image Pre-training for 3D Medical Imaging (opens in a new tab)
202515 papers
- AAAI (T1)3D Denoisers Are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation (opens in a new tab)
- NeurIPS (T1)Active Test-time Vision-Language Navigation (opens in a new tab)
- Nat.Foundation models for fast, label-free detection of glioma infiltration (opens in a new tab)
- ICORESHierarchical Decomposition Framework for Steiner Tree Packing Problem (opens in a new tab)
- ACL (T1)Interactive and Expressive Code-Augmented Planning with Large Language Models (opens in a new tab)
- NeurIPS (T1)KL Penalty Control via Perturbation for Direct Preference Optimization (opens in a new tab)
- KDD (T1)MapExplorer: New Content Generation from Low-Dimensional Visualizations (opens in a new tab)
- Findings of NAACLMASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows (opens in a new tab)
- NeurIPS (T1)MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges? (opens in a new tab)
- ICML (T1)Probing Visual Language Priors in VLMs (opens in a new tab)
- EMNLP (T1)Revisiting LLM Value Probing Strategies: Are They Robust and Expressive? (opens in a new tab)
- CVPR (T1)Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents (opens in a new tab)
202417 papers
- Findings of EMNLPAuto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents (opens in a new tab)
- NeurIPS (T1)AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents (opens in a new tab)
- NAACL (T1)Code Models are Zero-shot Precondition Reasoners (opens in a new tab)
- Findings of ACLDeep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning (opens in a new tab)
- ICML (T1)Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration (opens in a new tab)
- EMNLP (T1)Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (opens in a new tab)
- AAAI (T1)Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers (opens in a new tab)
- ACL (T1)Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination (opens in a new tab)
- CVPR WorkshopNICE: CVPR 2023 Challenge on Zero-shot Image Captioning (opens in a new tab)
- Findings of EMNLPProspector: Improving LLM Agents with Self-Asking and Trajectory Ranking (opens in a new tab)
- CVPR WorkshopShow, Think, and Tell: Thought-Augmented Fine-Tuning of Large Language Models for Video Captioning (opens in a new tab)
- Findings of ACLSmall Language Models Need Strong Verifiers to Self-Correct Reasoning (opens in a new tab)
202326 papers
- EMNLP (T1)A Picture is Worth a Thousand Words: Language Models Plan from Pixels (opens in a new tab)
- NeurIPS (T1)Combining Behaviors with the Successor Features Keyboard (opens in a new tab)
- ICLR (T1)Composing Task Knowledge With Modular Successor Feature Approximators (opens in a new tab)
- NeurIPS (T1)CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation (opens in a new tab)
- WACVEnriched CNN-Transformer Feature Aggregation Networks for Super-Resolution (opens in a new tab)
- ACL (T1)Few-shot Reranking for Multi-hop QA via Language Model Prompting (opens in a new tab)
- RepL4NLP@ACLFine-grained Text Style Transfer with Diffusion-Based Language Models (opens in a new tab)
- EMNLP (T1)From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning (opens in a new tab)
- ICML (T1)Go Beyond Imagination: Maximizing Episodic Reachability with World Models (opens in a new tab)
- Findings of EMNLPGRACE: Discriminator-Guided Chain-of-Thought Reasoning (opens in a new tab)
- AAAI (T1)Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters (opens in a new tab)
- NeurIPS (T1)Guide Your Agent with Adaptive Multimodal Rewards (opens in a new tab)
Where alumni went41 listed, 47% to industry
Industry 14Academia 16
Most often to
- Amazon
- Google DeepMind
Also to
- Stanford University
- NYU
- Princeton University
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
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