United States
I love understanding and explaining things clearly! At UCLA, I work on AI interpretability and cognitive science (though I actually think that AI interpretability is a field under the cognitive sciences!). Previously, I studied philosophy & cognitive science at Williams College and Oxford University (study-abroad). Google Scholar: https://scholar.google.com/citations?user=XuzjypEAAAAJ&hl=en
Mentors: Paul Reichers, Adam Shai at Simplex - MATS 10.0 scholar
Mentor: Mor Geva - Studying how LLMs learn how to do a task from few examples (a.k.a., in-context learning)
Mentor: Xavier Poncini at Simplex - Studying whether LLMs know and reason about the state of the world as it processes a user's prompt, using HMMs as a mathematically tractable case-study of world states.
Mentors: Rick Dale, Hongjing Lu, Alexia Galati - Furthered theory of interpersonal dynamics by developing a Bayesian dynamical system model explaining joint action/coordination.
Mentors: Hongjing Lu, Taylor Webb, Keith Holyoak - Improved accuracy by 10% on relation-based few-shot image classification using a graph-matching model. - Analyzed how LLMs perform relational reasoning by identifying attention circuits within the models. - Identified systematic failures of multimodal LLMs in classifying objects into correct relational roles, motivating graph-based models.
Mentors: Keith Holyoak, Hongjing Lu - Identified sources of generalizability in CNN representations trained on relation-based image classification. - Wrote paper arguing that analogical mapping results in generalizable human mental representations of relation-based concepts.
Mentors: Emily Conway, Jeremy Gwinnup, Grant Erdmann, Eric Hansen, Tim Anderson - Reduced video captioning error of video LLMs and scene-graph transformers by training on object- and relation-based captions. - Developed NLP pipeline to identify news trends of objects, relations, and semantic triplets (object-relation-object) in captions.