About Me
I work in the plasma group at KLA where I develop machine learning systems to examine semiconductor wafers.
I am broadly interested in building robust, performant and interpretable models. I deal with two main types of models, to paraphrase Belinda Li.
World models: of an external environment that support coherent downstream prediction.
Self models: an intelligent system's own internal computations, behaviors, and limitations.
In my spare time, I read, write and exercise.
I’m a fan of Annie Ernaux, Georg Trakl, EE Cummings, Jennifer Egan, Mahmoud Darwish.
Education
B.S.E. in Computer Science
University of Michigan, College of Engineering
Sept 2021 - May 2025
High School
Bronx High School of Science
Sept 2017 - May 2021
Interests
Awards & Scholarships
USA Computing Olympiad Silver
December 2020AIME Qualification
February 2020AMC 10 8th Place in School
February 2019Selected Research
All research →LoRAMBo: Fighting LoRA Memory Bottlenecks with Optimized Rank Selection
Liam Cawley
A theoretical framework unifying classical matrix approximation with curvature-aware rank allocation for LoRA. We derive offline and online algorithms with near-optimality guarantees for distributing ...
MetaRepICL: In-Context Learning as Kernel Regression on Learned Representations
Liam Cawley
Investigating whether transformer in-context learning implements kernel ridge regression on learned hidden representations. We construct explicit linear-attention-to-conjugate-gradient mappings and st...
StableGLM: Rashomon Sets for Generalized Linear Models
Liam Cawley
A toolkit for computing ε-Rashomon sets, membership certificates, and set-level interpretability metrics for GLMs. Addresses the question: when many models fit the data equally well, which explanation...