James Zou

[introductory/intermediate] Large Language Models and Biomedical Applications [videorecorded]

Summary

This series of lectures will discuss recent advances in large language models (LLMs) and applications to biomedicine and healthcare. We will first discuss the basics of how to train and align LLMs. Then we will investigate several interesting applications of LLMs to help patients, clinicians and researchers. We will also discuss state-of-the-art in making LLMs more reliable, trustworthy and cost-efficient.

Syllabus

• Introduction to LLM: pretraining, fine-tuning and human alignment.
• Building and using LLMs to help patients, clinicians and researchers.
• Responsible LLM: methods to improve reliability and reduce energy and cost usage.

References

https://www.nature.com/articles/s41591-023-02504-3

https://arxiv.org/abs/2307.09009

https://arxiv.org/abs/2305.05176

Pre-requisites

Machine learning basics.

Short bio

James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He is also the faculty director of Stanford AI4Health. He works on both improving the foundations of ML — by making models more trustworthy and reliable — as well as on in-depth scientific and clinical applications. Many of his innovations are widely used in tech and biotech industries. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe.

Note:

On June 8, the lecturer informs the organizers that he will not be able to participate in person due to unexpected circumstances.