Research Interest

Artificial intelligence
Signal Processing
Computer Vision

Biography

Dr. Shaghayegh (Shay) Chavoshian is a biomedical engineer and AI researcher with expertise spanning artificial intelligence, and signal processing in digital health. She is a postdoctoral researcher at the University of Toronto under the supervision of Prof. Alex Mihailidis, focusing on the early detection of cardiovascular, neurodegenerative, and hematological conditions using advanced signal processing and AI methods. She completed her BSc in Biomedical Engineering at Amirkabir University of Technology, followed by a direct-entry PhD in Biomedical Engineering at the University of Toronto. Her doctoral research focused on developing AI-driven approaches to reduce slips and falls. She has secured over $118,000 in research funding through more than 35 awards and scholarships supporting her doctoral studies. Her work was driven by a commitment to making AI in healthcare more equitable, interpretable, and impactful that is reflected in her recognitions for Indigenous research initiatives and women's clinical health research. She has also contributed to research and clinical innovation at the University Health Network (UHN) as an intern, research assistant, and graduate fellow since 2022 and continues to do so. Dr. Chavoshian has also fostered national and international research collaborations with teams at institutions including Tohoku University (Japan), the University of Ioannina (Greece), and the Institut de recherche Robert-Sauvé en santé et en sécurité du travail (Québec). She has held over 25 teaching assistant appointments at the University of Toronto, teaching courses in machine learning, computer science, circuit analysis, data science methods, and statistical learning, and was recognized with the 2024 Best Teaching Excellence Award. She contributes actively to the engineering and research community, serving as an IEEE technical session chair, a Professional Engineers Ontario committee chair, UHN STEM ambassador and co-lead of IEEE Canada’s indigenous membership development initiatives.