
Reza Samavi
Reza Samavi is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. He serves as a Faculty Affiliate with the Vector Institute for Artificial Intelligence, and he is Adjunct Associate Professor in the Faculty of Engineering at McMaster University. He received his PhD from the University of Toronto. His research focuses on information security, machine learning, and trustworthy AI, emphasizing the importance of ensuring that AI systems are trustworthy, robust and reliable when integrated into the fabric of society.
Throughout his career, Reza has received several honours, including being designated as the Privacy by Design Ambassador by the Information and Privacy Commissioner of Ontario and receiving the Privacy Technology Research Award from the IBM Center for Advanced Studies. As PI and co-PI, he has secured several grants from NSERC, SOSCIP, MITACS, HHS, IDEaS with more than 2 million dollars in funding. He is the director of the Trustworthy AI Research Lab (TAILab) at Toronto Metropolitan University, where he collaborates with students and researchers across Canada and around the globe, including the University of Toronto, McMaster University, University of British Columbia, University of Amsterdam and the University of Edinburgh, to explore the challenges at the intersections of security, privacy, and machine learning.
Recent Publications
Karimi, H., & Samavi, R. (2024). Evidential uncertainty sets in deep classifiers using conformal prediction (external link) . Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR, 230, 466–489.
Daneshvar, H., Boursalie, O., Samavi, R., Doyle, T.E., Duncan, L., Pires, P., & Sassi, R. (2024). SOK: Application of machine learning models in child and youth mental health decision-making (external link) . In S. Ben-David, G. Curigliano, D. Koff, B.A. Jereczek-Fossa, D. La Torre, and G. Pravettoni (Eds.), Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications, (pp. 113-132). Academic Press.
Teferra, B.G., Rueda, A., Pang, H., Valenzano, R., Samavi, R., Krishnan, S., & Bhat V. Screening for depression using natural language processing: Literature review (external link) . Interactive Journal of Medical Research, 13(1):e55067, 2024.