Date: 29 April 2026 (Wednesday)
Time: 2:00 pm – 3:00 pm
Venue: Lecture Theatre 2, G/F, William M.W. Mong Block, 21 Sassoon Road 
Host: Professor Guoping fan

Prof. Tian XU received his B.S. degree from Fudan University in 1982 and Ph.D. degree from Yale University in 1990. He was a postdoctoral fellow at University of California, Berkeley from 1990 to 1993. From 1993 to 2018, he was an Assistant Professor, Associate Professor (tenured in 2001), C.N.H. LONG Professor of Genetics at Yale University. Prof. Xu also served as the Vice Chairman of the Department of Genetics at Yale from 2003 to 2018 and the Special Advisor to President of Yale University from 2002 to 2013. He was also an Investigator of the Howard Hughes Medical Institute from 1997 to 2018. From 1997 to 2018, he served as Adjunct Professor and Director of the Institute of Developmental Biology, and Director of the International Collaboration and Research Center for Development and Disease at Fudan University. He has served as a Member of the Expert Advisory Committee of the Overseas Chinese Affairs Office of the State Council since 2011. Prof. Xu joined the Westlake University in 2018 as a Chair Professor of Genetics. He served as the Founding Vice President of Westlake University from 2018 to 2024.

The Xu lab is currently focusing on AI for Medicine and Science for AI. By combining PB screens for novel drug targets and AI medicinal chemist for drugs, a program of dozen clinical trials of first-in-class drugs are in phase I, II and III for both rare and common diseases. By developing algorism for analyzing transcriptomic data (Yuan et al., 2020), AI method for standardizing data (Zhang et al., 2026), and AI platform for curation (Yang et al., 2025), we have developed the world first Large Life Language Model, DeepNature, for diagnosis and therapy. The systematic generation of transcriptomic data for natural medicinal materials and the standardization of global drug and patient transcriptomic data allow DeepNature to predict potential therapeutics from both herbal and small molecules. In Science for AI, we have extended our pioneer work of genetic dissection of growth and tissue size control to dissect the neural basis for quantification and has identified the involvement and mechanism of PPC for quantification behavior in both mice and humans. The Neural Entropy model for quantification suggests a novel AI scheme. Recently, we have also developed the concepts and animal model for studying the abilities of tool using, a key challenge for future development of humanized AI robots.

All are welcome.