I am a first-year PhD student advised by Prof. Rose Yu and Prof. Loris D'Antoni. My research interests broadly lie at the intersection between machine learning and formal methods. Recently, I am particularly focusing on controllable generation with Large Language Models, with applications including (but not lmited to) AI4Science and Code Generation.
Previously, I have worked with Prof. Guy Van den Broeck at UCLA on leveraging Tractable Probabilistic Models for controllable generation.
Warning
Problem: The current name of your GitHub Pages repository ("Solution: Please consider renaming the repository to "
http://".
However, if the current repository name is intended, you can ignore this message by removing "{% include widgets/debug_repo_name.html %}" in index.html.
Action required
Problem: The current root path of this site is "baseurl ("_config.yml.
Solution: Please set the
baseurl in _config.yml to "Education
-
University of California, San DiegoDepartment of Computer Science and Engineering
Ph.D. StudentSep. 2025 - present -
Harvey Mudd CollegeB.S. in Computer Science and MathematicsAug. 2021 - May 2025
Experience
-
MicrosoftContract Software EngineerAug. 2024 - May 2025
Honors & Awards
-
Google CSRMP Awardee2023
-
HMC Dean's List2021-2025
News
Selected Publications (view all )
Learning Tractable Distributions of Language Model Continuations
Gwen Yidou-Weng, Ian Li, Anji Liu, Oliver Broadrick, Guy Van den Broeck, Benjie Wang
ArXiv 2025
We propose Learning to Look Ahead (LTLA), a hybrid approach that pairs the same base language model for rich prefix encoding with a fixed tractable surrogate model that computes exact continuation probabilities.
Learning Tractable Distributions of Language Model Continuations
Gwen Yidou-Weng, Ian Li, Anji Liu, Oliver Broadrick, Guy Van den Broeck, Benjie Wang
ArXiv 2025
We propose Learning to Look Ahead (LTLA), a hybrid approach that pairs the same base language model for rich prefix encoding with a fixed tractable surrogate model that computes exact continuation probabilities.
Steering LLMs’ Reasoning With Activation State Machines
Ian Li, Philip Chen, Max Huang, Andrew Park, Loris D'Antoni, Rose Yu
FoRLM @ NeurIPS 2025ArXiv 2025
We introduce Activation State Machine (ASM), an lightweight dynamic steering mechanism that learns the latent dynamics of ideal reasoning trajectories and applies context-aware interventions at inference time.
Steering LLMs’ Reasoning With Activation State Machines
Ian Li, Philip Chen, Max Huang, Andrew Park, Loris D'Antoni, Rose Yu
FoRLM @ NeurIPS 2025ArXiv 2025
We introduce Activation State Machine (ASM), an lightweight dynamic steering mechanism that learns the latent dynamics of ideal reasoning trajectories and applies context-aware interventions at inference time.