时间：2024年5月24日（周五） 14:00-15:30

Time: 14:00-15:30, Friday, May 24, 2024

地点：西湖大学云谷校区E10-304

Venue: E10-304, Yungu Campus

主持人: 西湖大学工学院 林涛 博士

Host: Dr. Tao Lin, Assistant Professor, Westlake University

语言：英文

Language: English

主讲嘉宾/Speaker：

Le Song

CTO & Chief AI Scientist

BioMap

个人简介/Biography:

Le Song is the CTO and Chief AI Scientist of BioMap. Le Song is a full professor in MBZUAI, and was a tenured associate professor of Georgia Institute of Technology, and the conference program chair of ICML 2022. He is an expert in machine learning and AI, among the top 2% highly cited researchers in the world, and has won many best paper awards in premium AI conferences such as NeurIPS, ICML and AISTATS. Recently, his work on using large language models for protein structure predictions has been featured as the cover story in Nature Machine Intelligence.

摘要/Abstract:

What will be the foundational AI models for biological systems? What data can be used to build them? How to build them exactly? Nowadays, biological data grow rapidly and converge into a few standard modalities, such as DNA, RNA and protein sequences and structures, biomolecular interaction networks, and single-cell RNA sequencing and imaging. It seems timely to ask the intriguing questions as to whether foundational AI models can be established for biological systems which possess certain level of generality and transferability and can serve as the infrastructure to enhance the entire spectrum of downstream prediction tasks from different scales of biological systems. 

In this talk, I will share my recent work along this direction and introduce the xTrimo family of large scale pretrained models leveraging a large amount of data from protein sequences, structures, protein-protein interactions, and single-cell transcriptomics. The pretrained models can be used as the foundation to address many predictive tasks arising from protein design and cellular engineering and achieve SOTA performances.

讲座联系人/Contact:

Ms. Songmei Zhu

zhusongmei@westlake.edu.cn