The rapid 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 the interdisciplinary integration of dynamics, systems science, data science, and artificial intelligence.

陈洛南, 本科毕业于华中科技大学电气工程专业, 之后就读于日本东北大学系统科学专业获得硕士和博士学位。1997年日本大阪产业大学副教授, 2000年美国加州大学洛杉矶分校(UCLA)访问教授, 2002年日本大阪产业大学教授; 2010年担任中科院生化细胞研究所(中科院分子细胞科学卓越创新中心)研究员, 国科大杭高院首席教授。2025年加入上海交通大学数学科学学院/人工智能学院, 任讲席教授。同时担任中国生物信息学学会-网络生物学专业委员会主任委员, 中国生化细胞学会-分子系统生物学专业分会主任委员, 中国运筹学会-计算系统生物学分会名誉理事长, IEEE SMC学会-系统生物学技术委员会主席。主要从事生物信息学, 非线性动力学, 人工智能等研究。近年来作为通讯或共同通讯作者发表400余篇期刊论文(Nature, Nature Genetics, Nature Communications, Nature Cancers, Cancer Cell, Cell Research, PNAS, NSR, JACS, PRL, Innovation等)和五部专著。