Jingcheng Hu
MEng
Application of Deep Learning in Modeling of Bubble Evolution
email: jingcheng.hu@mail.utoronto.ca
Biosketch
Jingcheng Hu is a graduate student in Mechanical Engineering at the University of Toronto, with research interests at the intersection of computational modeling, machine learning, and robotics. His background includes experience in solid mechanics simulations, experimental methods, and embedded systems. His research focuses on developing data-driven models to analyze bubble dynamics on nano-structured electrodes, leveraging machine learning techniques such as Convolutional Neural Networks (CNNs) for data-driven prediction and analysis. Jingcheng is currently collaborating with Amirreza Azad on this project, contributing his expertise in CNN-based analysis, prediction optimization, and experimental integration.
Research
My research focuses on understanding bubble dynamics on nano-structured electrodes, a phenomenon critical for optimizing electrochemical reactions and energy conversion efficiency. I aim to develop data-driven models to predict bubble behavior and guide experimental design.