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Reza Samavi

Associate Professor

Reza Samavi is currently an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering at Toronto Metropolitan University and a faculty affiliate with the Vector Institute for Artificial Intelligence. He received his PhD from University of Toronto in 2013 and worked as an assistant professor and the eHealth graduate program coordinator at the Department of Computing and Software at McMaster University between 2014-2020. Reza's research interests include Data Security & Privacy, Safety & Security of Machine Learning Algorithms, and Trustworthy AI. For his research on AI and Security, Reza has received several research grants from NSERC, SOSCIP, MITACS, HHS, IDEaS. For his research on information privacy he has received the Privacy Technologies Research Award from IBM and the Privacy By Design Research Award from the Information and Privacy Commissioner of Ontario.

RESEARCH INTERESTS

  • Information Security
  • Machine Learning
  • Trustworthy AI

 

AWARDS AND RECOGNITION

  • Faculty Affiliate, Vector Institute
  • Privacy Technologies Research Award, IBM
  • Privacy By Design Research Award, Information and Privacy Commissioner of Ontario

 

SELECTED PUBLICATIONS

  • Sutton, Andrew, and Reza Samavi. "Tamper-Proof Privacy Auditing for Artificial Intelligence Systems." IJCAI, pp. 5374-5378. 2018.
  • Liang, Yuting, and Reza Samavi. "Optimization-based k-anonymity algorithms." Computers & Security 93 (2020): 101753.
  • Samavi, Reza, and Mariano P. Consens. "Publishing privacy logs to facilitate transparency and accountability." Journal of Web Semantics 50 (2018): 1-20.
  • Sutton, Andrew, and Reza Samavi. "Blockchain enabled privacy audit logs." International Semantic Web Conference, pp. 645-660. Springer, Cham, 2017
  • Boshra, Rober, Kiret Dhindsa, Omar Boursalie, Kyle I. Ruiter, Ranil Sonnadara, Reza Samavi, Thomas E. Doyle, James P. Reilly, and John F. Connolly. "From group-level statistics to single-subject prediction: machine learning detection of concussion in retired athletes." IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 7 (2019): 1492-1501