Abstract: 

The recent development of high-throughput omics technologies has provided unprecedented big data support for life science research. Biomedical data from multiple sources, dimensions, and scales constitute typical multi-source heterogeneous big data, exhibiting significant spatiotemporal dynamic characteristics. In response to this feature, there is an urgent need to develop a system of dynamic theories and AI methods that can accurately characterize the spatiotemporal evolution rules of data, including tipping point detection and early warning prediction based on dynamic systems and AI, time series prediction based on the low-dimensional characteristics of attractors, causal inference based on embedding theory, and AI-enabled nonlinear multimodal data fusion based on deep learning. These new data science theories and AI methods centered on dynamics can not only help understand and predict the dynamic behaviors of complex systems and analyze their intrinsic processes and mechanisms but also provide a more physically interpretable modeling paradigm for artificial intelligence. Thus, they form a mutually promoting research paradigm of AI for Science (AI4Science) and Science-driven AI (Science4AI). The relevant theories and algorithms can be widely applied to key scenarios such as early warning of tumor invasion, metastasis and recurrence, real-time monitoring of public health, sub-health risk assessment, time series prediction, and trusted AI construction, which is of great significance for promoting "dynamical systems biology", and further the interdisciplinary integration of dynamics, systems science, data science, and artificial intelligence.  

Bio: Luonan Chen is a Chair Professor in 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 300 journal papers and four monographs (books) in the area of bioinformatics, nonlinear dynamics and mathAI4Science with H-index 96.