This talk will focus on problems that lie at the intersection of two timely topics, namely data science and complex systems. For example, how to estimate the position of a target in indoor settings where GPS is unavailable? How to reconstruct the paths of diffusion from macroscopic observables? How to tell and test for cause-and-effect using time series data collected from a high-dimensional complex system? All of these problems, and potentially many more, can be interpreted as a (data-driven) inverse problem and arises naturally in the context of nonlinear dynamics and complex systems, two areas of research that can benefit from the data science revolution. I will then discuss how to solve some of these problems using tools and techniques often seen in modern machine learning, including optimization, functional learning, regression, information theory. Finally, the talk will include a set of examples of application such as indoor localization using power measurements, noninvasive damage detection, as well as inference of interaction networks in collective animal behavior and data-driven learning of nonlinear dynamical systems under noisy observations.
 

Dr. Jie Sun is a tenured Associate Professor at Clarkson University, USA, with affiliation at three departments: Mathematics, Physics, and Computer Science. He is an Editor of Chaos and Associate Editor of Mathematics in Science and Industry. In 2018, he was selected as a Distinguished Fudan Fellow by Fudan University. Before joining Clarkson University, Sun was a postdoc at Northwestern University and Princeton University from 2010 to 2012. Sun obtained his B.Sc in Physics at Shanghai Jiao Tong University in 2006 and PhD in Mathematics at Clarkson University in 2009. Dr. Sun’s current research mainly focuses on data-enabled science and their applications such as causal network inference, information flow, sensor localization and others that emerge from nonlinear dynamics and complex systems. Sun has over 40 publications including several in Physical Review Letters, Physical Review X, and SIAM series, and some of his work were featured in Bloomberg and The Guardian.