Behzad Abdi is a Ph.D. candidate in electrical engineering at McMaster University, conducting research in artificial intelligence and deep learning for smart traffic applications under the supervision of Dr. Ali Emadi at the McMaster Automotive Resource Centre (MARC). His expertise spans trajectory prediction, computer vision, natural language processing, and graph neural networks. He holds B.Sc. and M.Sc. degrees from Sharif University of Technology and brings two years of industry experience as a design engineer. At McMaster, Behzad played a key role in the MOBILITYCUBE program, leading two AI-driven traffic safety projects and supporting 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 uncover how they worked. This early curiosity grew into a lifelong passion, leading him to pursue a focused academic and professional path in electrical engineering.
He earned both his B.Sc. and M.Sc. degrees from Sharif University of Technology in Tehran, where he built a strong foundation in power electronics, simulation, and signal processing. During his master’s studies, Behzad specialized in condition monitoring for power converters and simultaneously worked full-time as a design engineer at a startup. There, he gained hands-on experience in circuit design, embedded systems, and medical device compliance.
In May 2022, Behzad began his Ph.D. in electrical engineering at McMaster University, where his research centres on intelligent transportation systems—particularly trajectory prediction using deep learning. His work combines AI, computer vision, and real-time data processing to enhance traffic safety.
As part of the MOBILITYCUBE program, Behzad served as the assistant program manager, helping to coordinate multidisciplinary projects that bridge academic research and industry applications. In addition to his management role, he led two key initiatives focused on real-time trajectory prediction and V2X-enhanced perception. His leadership was instrumental in the development of intelligent traffic infrastructure prototypes.
Technically proficient in C, C++, Python, and PyTorch, Behzad brings deep expertise in AI techniques including NLP, computer vision, and GNNs. He is especially passionate about translating cutting-edge AI research into scalable, real-world solutions that make transportation systems safer and smarter.
