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Postdoctoral research fellow
Company Information
UC San Francisco is the leading university dedicated to advancing health worldwide through preeminent biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care.
Within our overarching advancing health worldwide mission, UCSF is devoted at every level to serving the public.
The unprecedented ability of large language models (LLMs) to interpret text data with human-like reasoning is poised to transform many fields. Nevertheless, for LLMs to be safe and effective for use in high-risk domains like healthcare, it is crucial to understand biases embedded in this technology, as it has been shown to vary in performance across subgroups and even discriminate against minorities. This project aims to study and develop statistical methods for auditing LLMs and to understand the limits of current approaches. Methodologies developed in this project will be tested on real-world clinical data, including unstructured notes. This project is a supplement of our existing PCORI project "``Diagnostic Tools for Quality Improvement of Machine Learning-Based Clinical Decision Support Systems" (see parent project description here: https://www.pcori.org/research-results/2022/diagnostic-tools-quality-improvement-machine-learning-based-clinical-decision-support-systems).
We are seeking a postdoctoral researcher to join our lab. The primary responsibilities are:
* Rigorously analyze and evaluate existing algorithms for auditing LLMs, such as those involving red-teaming
* Develop new statistical methods/frameworks for comprehensive auditing of LLMs
* Develop an explanation framework and statistical inference procedures to understand systematic limitations of LLMs
* Write, edit, and publish research manuscripts in collaboration with the team
The position requires at least a PhD degree in data science, (bio)statistics, computer science, or another relevant field. We are looking for someone who:
* has experience in training and testing ML algorithms for large datasets
* has experience in natural language processing and working with LLMs
* has experience in methodological development and can perform independent research, with a strong and relevant publication record
* has strong software engineering background (e.g. python, torch, huggingface, git-based workflows, high-performance computing, SQL, spark)
* is able to work collaboratively with a team