Summary
Hojjat Salehinejad is a machine learning scientist and engineer with over a decade of experience building ML infrastructure and models for healthcare, human–machine interaction, and FinTech. Holding a PhD in Machine Learning from the University of Toronto and a postdoc focused on RF-based activity recognition, he now applies multimodal and wireless sensing approaches at Mayo Clinic to improve patient outcomes and near‑real‑time clinical workflows. He combines research rigor—evidenced by academic appointments and IEEE editorial/service roles—with hands-on production skills in Python, PyTorch, Docker, Kafka and distributed systems. His work spans energy‑efficient models for edge devices to large‑scale streaming and medical‑imaging pipelines, and he routinely partners with clinicians to translate models into deployed tools. An understated strength is his track record of compressing and optimizing deep models for constrained hardware, enabling practical deployment in clinical and IoT settings.
10 years of coding experience
10 years of employment as a software developer
Master of Applied Science (MASc) Electrical Computer and Software Engineering, Master of Applied Science (MASc) Electrical Computer and Software Engineering at Ontario Tech University
Postdoctoral Fellowship Machine Learning, Postdoctoral Fellowship Machine Learning at University of Toronto
Bachelor of Applied Science (B.A.Sc.) Electrical and Computer Engineering, Bachelor of Applied Science (B.A.Sc.) Electrical and Computer Engineering at Shahid Bahonar University of Kerman
English, French, Persian