报告人：美国Clarkson大学孙杰教授 
时间：2019年1月21日（周一）上午10:00-11:00
地点：实验十五楼207
题目：Data-driven Inverse Problems in Complex Systems

摘要：

In this talk, I will discuss two timely research topics, namely data science and complex systems, and show that there are many interesting problems that arise at their intersection. For example, how can one find 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 systems? I will show that all these problems, and potentially many more, can all be abstracted as a data-driven inverse problem. I will then discuss how to solve some of these problems via tools and techniques from optimization, functional learning, regression, and 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.

报告人介绍：

孙杰教授，目前是美国Clarkson大学数学系副教授（终身），并担任Chaos期刊编委。他于2006年本科毕业于上海交通大学物理系，2009年博士毕业于Charkson大学数学系，此后在2010至2012年间在美国Northwestern大学和Princeton大学担任博士后，并于2012年7月回Clarkson任教至今。研究领域主要为复杂系统和数据科学，包括复杂系统中的因果关系判定和分析、传感器网络定位、复杂网络控制、网络动力学同步以及优化设计。曾在多个数学、物理、以及交叉学科期刊发表文章，例如Physical Review Letters、Physical Review X、SIAM Journal on Applied Dynamical Systems等。研究成果曾被美国Bloomberg、英国The Guardian等知名媒体等报道。