AI4Science: Industrial Applications and Foundation Models for Scientific Discovery
- Dr. Kai Liu
- Time: 2025-06-10 14:00
- Host: Dr. Hao Dong
- Venue: Room 204, Courtyard No.5, Jingyuan
Abstract
In recent years, AI for Science (AI4Science) has emerged as a transformative paradigm, driving advances across diverse domains such as drug discovery, materials science, chemical synthesis, mathematics, and machine learning itself. Many of the most pressing scientific questions today are increasingly data-driven—whether arising from molecular graph representations, multi-modal biomedical data, or high-dimensional pathological images. In this landscape, AI is rapidly becoming the “third pillar” of scientific discovery, complementing theoretical reasoning and experimental validation.
In this talk, I will present real-world case studies from industry research, particularly in biotechnology and materials science, to illustrate how AI is reshaping scientific workflows. I will discuss how multi-omics patient data can be mined using machine learning to reveal novel drug targets and enable personalized therapies. I will also highlight how equivariant neural networks can approximate Density Functional Theory (DFT) to accurately predict molecular properties and accelerate material design.
Biography
Dr. Kai Liu is a recognized AI4Science leader with over a decade of experience in machine learning, large language models (LLMs), and scientific discovery. As Vice President of Artificial Intelligence at SES AI, he led the AI team in advancing battery materials discovery, including the development of OmniScience, a 70-billion-parameter scientific foundation model that set new standards in domain-specific reasoning. Previously, as Director of Artificial Intelligence at Genentech, Dr. Liu founded and led AI teams focused on multimodal omics learning, immunogenicity prediction, and natural language processing, driving innovation in drug discovery, biomarker development, and clinical trial design. He has published in leading journals and conferences including Nature, Nature Communications, Scientific Reports, and NeurIPS etc. Dr. Liu holds a Ph.D. in Neuroscience and an M.S.E. in Machine Learning from Johns Hopkins University.