Papers
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Concept Bottleneck Models with LLM Priors
preprint, 2024
This work eliminates the need for human-annotated concepts by proposing a novel method to learn concepts by wrapping LLMs within a Bayesian framework. This approach is highly generalizable across various data modalities and allows for rigorous uncertainty quantification despite LLMs being prone to error and hallucinations.
Prediction without Preclusion: Recourse Verification with Reachable Sets
ICLR (Spotlight), 2024
Individuals can be assigned predictions that they cannot change through actions on their features. This paper investigates and formalizes scenarios of predictions without recourse. We argue the importance of these scenarios for both model development and recourse detection methods.
Bayesian Priors From Large Language Models Make Clinical Prediction Models More Interpretable
AMIA Podium Abstract, 2024
Implementing a Predictive Model to Reduce Hospital Readmissions in a Safety Net Healthcare System
ML4H Demo Track, 2024
Industry Experience
Teaching Experience
Service
- Vision 1948 (May 2023 – Present)
- PenPal for the Incarcerated (September 2020 – Present)
- Warren Community Garden (August 2021 – Present)
- UCSF AI4All (July 2024 – July 2024)
- The Recyclery (August 2018 – May 2021)