Shenshen Gu: Neural networks and deep learning
The applications of neural networks and deep learning
Shanghai, China: Shanghai University
Department of Automation
Dr. Shenshen Gu was born in Shanghai, China. He received his Ph.D. degree in Automation and Computer-Aided Engineering from the Chinese University of Hong Kong in 2009. He then joined the School of Mechatronics Engineering and Automation at Shanghai University, where he is currently an associate professor. Dr. Shenshen Gu’s research interests include the applications and theories of operations research, neural networks, and deep learning.
Recent advances and emerging approaches in neural model and learning (i.e. deep neural network) have led to an unparalleled surge of interest in the topic of neural networks. Neural networks provide an intelligent approach for solving complex problems that might otherwise not have a tractable solution. Neural networks have emerged as a powerful tool by providing outstanding performance that allow a wide variety of unprecedented applications in associative memory, function approximation, optimization problem, nonlinear system modelling and control. Neural networks themselves are typically nonlinear, and many different kinds of neural network models have recently been proposed for solving emerging problems. The research will focus on following issues: 1. Designing, modelling and analyzing the deep neural networks for some specific applications. 2. Training and testing the proposed deep neural networks models. 3. The implementations of deep neural networks on some embedded systems. The research will provide theoretical basis and feasible approaches for artificial intelligence, and new techniques for neural networks and deep learning.