报告题目： Dynamics-based data science in biology and medicine (动力学的数据科学与AI应用)

报告人：陈洛南 中国科学院

报告时间：4月7日 17:00-18:00

报告地点：数学院 135

报告摘要：In this talk, I will present a new concept "dynamics-based data science" in AI applications of biology and medicine for studying dynamical processes and disease progressions, including dynamic network biomarkers (DNB) for early-warning signals of critical transitions, spatial-temporal information (STI) transformation for short-term time-series prediction, and partial cross-mapping (PCM) for causal inference among variables. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamics-based data-driven approaches as explicable, quantifiable, and generalizable. In particular, dynamics-based data science approaches exploit the essential features of dynamical systems in terms of data, e.g. strong fluctuations near a bifurcation point, low-dimensionality of a center manifold or an attractor, and phase-space reconstruction from a single variable by delay embedding theorem, and thus are able to provide different or additional information to the traditional approaches, i.e. statistics-based data science approaches. The dynamical-based data science approaches will further play an important role in the systematical research of various fields in biology and medicine as well as AI.

报告人简介：

陈洛南教授，华中科技大学电气工程学士学位，日本东北大学系统科学硕士学位，日本东北大学系统科学博士学位。曾任美国加州大学洛杉矶分校(UCLA)访问教授，日本大阪产业大学教授，2009年4月起任日本东京大学教授(兼)，2010年4月至今任中科院系统生物学重点实验室执行主任，研究员。国家重点研发计划首席科学家，中国运筹学会首届会士，中国运筹学会计算系统生物学分会名誉理事长，中国生物化学与分子生物学会分子系统生物学专业分会主任委员，IEEE-SMC系统生物学委员会主席等。主要从事复杂网络、计算系统生物学、大数据分析和人工智能等方面的研究工作。近年来，在系统生物学和复杂网络等研究领域发表了400余篇期刊论文及10余部编著书籍(H-index > 75; Elsevier高被引）