Behzad Abdi is a Ph.D. candidate in Electrical Engineering at McMaster University, researching Artificial Intelligence and Deep Learning for Smart Traffic applications under the supervision of Dr. Ali Emadi at the McMaster Automotive Resource Centre (MARC). He specializes in trajectory prediction, computer vision, NLP and GNN learning models. Behzad holds B.Sc. and M.Sc. degrees from Sharif University of Technology, where he also gained two years of industry experience as a design engineer. At McMaster, he contributed to the MOBILITYCUBE program as he led and owned two AI-driven traffic safety projects and supported broader coordination efforts as a Program Assistant Manager.
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From a young age, Behzad Abdi was drawn to electronics and engineering, often dismantling household devices to understand their inner workings. This innate curiosity evolved into a lifelong passion, guiding him through an academic and professional journey in electrical engineering.
He earned his B.Sc. and M.Sc. from Sharif University of Technology, Tehran, where he developed a strong foundation in power electronics, simulation, and signal processing. During his master’s program, Behzad focused on condition monitoring in power converters and worked as a full-time design engineer at a startup, gaining practical experience in circuit design, embedded systems, and medical device compliance.
In May 2022, he joined McMaster University as a Ph.D. student and began research on intelligent transportation systems, with a specific emphasis on trajectory prediction using deep learning. His work integrates AI and computer vision for traffic safety.
As part of the MOBILITYCUBE program, Behzad served as Program Assistant Manager coordinating multi-disciplinary projects that bridge academic and industrial efforts. He also led and owned two critical projects involving real-time trajectory prediction and V2X-enhanced perception. His leadership contributed directly to the development and success of intelligent traffic infrastructure prototypes.
Behzad is proficient in C, C++, Python, PyTorch, and a wide range of AI techniques, including NLP, computer vision, and GNNs. He is passionate about translating advanced AI research into deployable solutions for safer and smarter transportation systems.
