About Me
Greetings! My name is Shutong Qi.
I am currently in my fourth year as a PhD student in the Department of Electrical and Computer Engineering at the University of Toronto. My advisor is Professor Costas D. Sarris. Prior to my studies at the University of Toronto, I received my bachelor’s degree from the Department of Electronics and Information Engineering at Beihang University in July 2020. I was honored to be recognized as an Excellent Graduate at my graduation ceremony.
Research Experience
My research focuses on the application of scientific machine learning (SciML) to address challenges in electromagnetics. I believe that SciML represents a fundamental aspect of artificial intelligence (AI) and has the potential to drive breakthroughs and significant progress in computational physics. The abundance of data generated by scientific user facilities offers ample opportunities for leveraging the power of SciML in this field.
From 2018 to 2020, I served as an undergraduate researcher at the EMCTI, under the guidance of Professor Qiang Ren. Our research focused on using deep learning to improve the efficiency of computational electromagnetics methods. In the summer of 2019, I had the opportunity to work as a visiting student researcher at the XDiscovery Lab at Dartmouth College, where I collaborated with Professor Xing-Dong Yang and Te-yen Wu on the development of smart cloth technology.
Previously, I had experience in signal processing in the project ADS-B and my first research project Multi-Antenna OFDM.
Recent News
[1/2024] Our paper Hybrid Physics-Informed Neural Network for the Wave Equation with Unconditionally Stable Time-Steppin has been accepted by the Antennas and Wireless Propagation Letters. I would like to extend my sincere gratitude to my supervisor, Professor Sarris, for his support and guidance throughout the process.
[3/2023] Our paper Physics-Informed Neural Networks for Multiphysics Simulations: Application to Coupled Electromagnetic-Thermal Modeling has been accepted by the 2023 IEEE/MTT-S International Microwave Symposium.
[1/2023] Our paper Electromagnetic-Thermal Analysis With FDTD and Physics-Informed Neural Networks has been accepted by the IEEE Journal on Multiscale and Multiphysics Computational Techniques.
[10/2022] Our paper Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on their Layouts has been accepted by the IEEE Transaction on Microwave Theory and Techniques. Many thanks to my supervisor Professor Sarris.
[7/2022] Our paper Numerical Dispersion Compensation for FDTD via Deep Learning gets accepted by AP-S 2022. I will give a talk in the session ‘In Remembrance of Allen Taflove: FDTD Pioneer and Educator’.
[10/2021] Our book Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning has been published by Springer. Many thanks to all the coauthers!
[09/2020] Start my PhD journey at University of Toronto.
[07/2020] Graduate from Beihang University with an Excellent Graduate honor.