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Lee Lab @ UW Madison

We sincerely appreciate the support provided by our sponsors: NSF, Amazon, and FuriosaAI!

Research focus: LLMs (theoretical/empirical analysis to understand how/why they work and improve them)

New Preprints

  • Arxiv’25 / In-Context Learning with Hypothesis-Class Guidance
  • Arxiv’25 / LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization
  • Arxiv’25 / VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
  • Arxiv’25 / Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges

Selected Work

Selected Talks

  • (Mar. 2025) Helmholtz/ELLIS Workshop on Foundation Models in Science, Berlin, Germany
    Title: Bridging Large Language Models and Classical Machine Learning: From LIFT to LLM-Lasso for Predictive Modeling
  • (Mar. 2025) EnCORE Workshop on Theoretical Perspective on LLMs, San Diego, CA
    Title: Beyond Decoder-Only Next Token Prediction (video)
  • (Feb. 2025) ECE Grad Seminar at the University of Pittsburgh, Feb. 2025.
    Title: Beyond Decoder-Only Next Token Prediction
  • (Nov. 2024) Seminars on AI Core and Applications, Seoul National University
  • (Oct. 2024) Mathematical Principles in Foundation Models, 2024 SIAM Conference on Mathematics of Data Science
    Title: Dual Operating Modes of ICL
  • (Apr. 2024) The Johns Hopkins University CIS/MINDS seminar
    Title: Theoretical Exploration of Foundation Model Adaptation Methods
  • (Mar. 2024) The 58th Annual Conference on Information Sciences and Systems @ Princeton University
    Title: A Probabilistic Framework for Understanding In-Context Task Learning and Retrieval
  • (Feb. 2024) 2024 Information Theory and Applications Workshop
    Title: The Expressive Power of Low-Rank Adaptation (LoRA)
  • (Feb. 2024) Foundations of Data Science - Virtual Talk Series @ UCSD/NSF TRIPODS Institute on Emerging CORE Methods in Data Science (EnCORE)
    Title: Theoretical Exploration of Foundation Model Adaptation Methods (video)
  • (Dec. 2023) CSP Seminar @ University of Michigan
    Title: Towards a Theoretical Understanding of Parameter-Efficient Fine-Tuning (and Beyond) (video)
  • (Nov. 2023) Efficient ML workshop @ Google Research New York
    Title: The Expressive Power of Low-Rank Adaptation (LoRA)

Openings

  • [postdocs] We are looking for a postdoc interested in the theoretical and algorithmic aspects of LLMs. If you want to work with us, please email me your CV and a research statement. I strongly recommend reading our lab’s recent papers.
  • [PhD students] We are looking for PhD students (ECE or CS) interested in the theoretical and algorithmic aspects of LLMs. If you want to work with us, please email me your CV and a research statement. I strongly recommend reading our lab’s recent papers.
  • [MS students] I am not currently looking for MS students.
  • [undergraduate students] Please take my courses first. I teach various machine learning courses (ECE 532, 539, 561, 570, 761, 901, …).