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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.
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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
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MicrosoftContract Software EngineerAug. 2024 - May 2025
Honors & Awards
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Google CSRMP Awardee2023
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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.