Abstract: In this talk, I will introduce some of our recent works on time series analytics, including directional interactions detections, dynamics predictions, and change-point detection. Based on the theory of nonlinear dynamical systems as well as machine learning techniques, we develop several data-driven and model-free frameworks for realizing detections and predictions. Through comparing our frameworks with other existing methods in the literature, we show the advantages of our frameworks when they are used to deal with the data produced synthetically by dynamical oscillators and collected by real experiments as well.
Speaker Introduction: Dr. Lin is a Full Professor in applied mathematics at Fudan University. Currently, he is serving as the Dean of the Research Institute of Intelligent Complex Systems, and as the Director of the Centre for Computational Systems Biology and the Vice Dean of the ISTBI, Fudan University, China. Now, he is acting as an AE of the IJBC, and a member of Editorial Advisory Board of CHAOS. His current research interests include bifurcation and chaos theory, stochastic systems and complex networks, data assimilation, causality analysis, and their applications to systems biology and artificial intelligence.