报告人：陈洛南研究员

报告题目：Dynamics-based machine learning (动力学的机器学习方法)

报告摘要：In this talk, I will present a new concept dynamics-based machine learning for studying dynamical processes and disease progressions, including spatial-temporal information (STI) transformation for short-term time-series prediction, and dynamical causality with embedding entropy (EE) 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 machine learning 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 machine learning approaches. The dynamical-based machine learning approaches will further play an important role in the systematical research of various fields including biology and medicine.

报告时间：4月23日下午14:30-15:30

报告平台：腾讯会议，会议号 347-5897-2265

报告人简介：陈洛南，华中科技大学电气工程学士学位；获日本东北大学系统科学硕士学位；获日本东北大学系统科学博士学位。1997年起任日本大阪产业大学副教授；2000年起任美国加州大学洛杉矶分校(UCLA)访问教授；2002年起任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月至今任中国科学院分子细胞科学卓越创新中心研究员，中科院系统生物学重点实验室执行主任；国科大杭高院首席教授。现任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，中国运筹学会《计算系统生物学分会》名誉理事长。主要从事计算系统生物学、大数据分析和人工智能的研究工作。近年来，在系统生物学和复杂网络等研究领域发表了350余篇期刊论文及10余部编著书籍(Citation > 20000; H-index > 70)。