Roksana received her B.S. and MSc. degrees in Electronic Engineering from the University of Isfahan, Isfahan, in 2016 and 2019, respectively. Her MSc. thesis was focused on "face detection" algorithms and the implementation of machine learning algorithms on FPGA. In 2022, she commenced her Ph.D. at Western University, London, ON, Canada. Her doctoral research primarily concentrates on improving Level 3 autonomous driving through extensive research on various "gaze estimation" and "driver's action recognition" models. Since 2022, she has been an active member of the IEEE Young Professionals, the IEEE Membership program, and the IEEE Women in Engineering initiative. 

Mojtaba Nabipour, a member of IEEE, received his B.Sc. in engineering from Noshirvani University of Technology, Babol, Iran, in 2009. He later completed his M.Sc. at Tarbiat Modares University, Tehran, Iran, in 2015. As of January 2023, he is pursuing a Ph.D. in the Electrical and Computer Engineering department at the University of Western Ontario (UWO) in London, ON, Canada. His primary research interests include computer vision, machine learning applications, and advanced manufacturing. 

Michael received his BESc in Software Engineering in 2024 from Western University, Canada, and is currently pursuing a master's degree in Electrical and Computer Engineering with a Collaborative Specialization in Artificial Intelligence at Western University. His current research focuses on in-vehicle facial detection systems using deep learning and computer vision techniques. 

Elbert is currently pursuing a Master of Engineering Science (MESc) degree in Software Engineering at Western University, following the completion of his Bachelor of Engineering Science in Spring 2025. His graduate research, in collaboration with the Robarts Research Institute, centers on the critical area of uncertainty-aware deep learning models for medical image segmentation in the analysis of kidney ablations. Beyond his graduate studies, Elbert has demonstrated strong practical development skills as an Undergraduate Research Assistant with the Free Appropriate Sustainability Technology (FAST) Research Group. In this role, he was responsible for developing the initial full-stack prototype of a specialized web application. This application integrates a Vision Transformer (ViT) model, built using PyTorch, for disease detection in strawberries. The tool is publicly available ( https://strawberry.uwo.ca/ ) and serves as a vital resource for local London farmers to improve crop health monitoring.

Wenyi received her Master's at the Department of  Electrical & Computer Engineering, Western University, with a collaborative specialization in artificial intelligence, and Bachelor of Computer Science from Dalhousie University in 2023, with a minor in Mathematics and an undergraduate certificate in Aritifical Intel & Intelligt Sys. Her professional experience includes an internship at Tencent as a project manager assistant and a role as a research assistant volunteer at Mount Sinai Hospital. Her ongoing project is to detect emergency vehicle approaching a self-driving car.