Speaker

 Luonan Chen

Time

2025.12.17 16:00-17:30

Abstract

I will present our recent works on "Dynamical Data-Science and AI Applications" , including dynamic network biomarkers (DNB) for early-warning signals of critical transitions, spatial-temporal information (STI) transformation for short-term time-series prediction, partial cross-mapping (PCM) for causal inference among variables, and further AI4Science and Biology4AI. These methods are all data-driven or model-free AI approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamical data-driven approaches with AI for phenotype quantification as explicable, quantifiable, and generalizable. The dynamical data-science and optimization approaches with AI for the quantifications of phenotypes will further play an important role in the systematical research of various fields in biology and medicine.  

Bio

Luonan Chen  received BS degree in the Electrical Engineering from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering from Tohoku University, Sendai, Japan, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. From 2010, he wa a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences; Chair Professor of Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences. Since 2025, He has been a chair professor at School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. In recent years, he published over 400 journal papers and four monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning.