学术报告第201835期

报告题目：Dynamic Network Reconstruction: Identifiability, Sampling and Sparsity

报 告 人：岳作功

报告时间：2018年7月3日 9:00

报告地点：南一楼中311

报告摘要：

Dynamic network reconstruction refers to a class of problems that explore causal interactions between variables operating in networked dynamical systems. Our work addresses methods on inferring network topology or dynamics from observations of an unknown system. The essential challenges, compared to system identification, are imposing sparsity on network topology and network identifiability. This work studies the following three cases: low-sampling-frequency data, multiple experiments with heterogeneity, and nonlinearity, which are concrete but generic features of biological signals that make reconstruction in practice particularly challenging.This presentation will give an quick overview of network reconstruction methods and focus on one of the major issue: low sample frequencies. In a sum, with considerations on signal features in practice, our work dedicates to contributing to useful reconstruction methods in practice and accelerating biological applications.

报告人简介：

Zuogong YUE (岳作功), received a bachelor degree in mechatronics engineering from Zhejiang University, and a master degree in mechanical engineering from Hong Kong University of Science and Technology. He joined the Group of Systems Control in Luxembourg Centre for Systems Biomedicine in 2014 and received his engineering degree in 2018.

His research interests focus on system identification and causal network

reconstruction (in a system-theoretic perspective), with additional tools

adopted/modified from convex optimization, statistics, stochastic analysis and machine learning, including but not limited to, Bayesian analysis, sampling methods, kernel methods and etc.