Ebrahim Ghafar-Zadeh
Associate Professor, Undergraduate Program Director - Engineering, P.Eng
Department:
Electrical Engineering & Computer Science
Email: egz@yorku.ca
Website: https://biosa-lab.ca/
Bio
Ebrahim Ghafar-Zadeh received the B.Sc. degree in Electrical Engineering from K. N. Toosi University of Technology, Tehran, Iran, in 1994, the M.Sc. degree from the University of Tehran, Tehran, Iran, in 1997, and the Ph.D. degree from Polytechnique Montréal, Montréal, QC, Canada, in 2008. His graduate research focused on CMOS-based sensors and lab-on-chip platforms. In recognition of his research achievements, he received multiple competitive fellowships (2008–2011), including the Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship (PDF) and Industrial Research and Development Fellowship (IRDF), as well as provincial and Québec-based awards (including FQRNT and ReSMiQ). He then held postdoctoral appointments in the Department of Electrical Engineering at McGill University, Montréal, QC, Canada, and in the Department of Bioengineering at the University of California, Berkeley, CA, USA.
In 2013, he joined the Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, Canada, as an Assistant Professor. He is currently an Associate Professor, Vice Chair, and Undergraduate Program Director (Engineering), and serves as Director of the Biologically Inspired Sensors and Actuators (BioSA) Laboratory. Since 2013, he has published more than 250 journal and conference papers, authored four books, and filed three patents. His research focuses on translational microelectronic and biosensing technologies for clinical diagnostics and environmental monitoring, supported by NSERC, CIHR, NFRF, Mitacs, CMC Microsystems, and other agencies and partners. He is a licensed Professional Engineer in Ontario (P.Eng.), a Senior Member of IEEE (SMIEEE), and a Member of Connected Minds.
Research Interests
- BioSA: Biologically Inspired Integrated Sensors and Actuators
- CMOS: CMOS Sensors, Circuits, and Systems for Life Science Applications
- PoCD: Point-of-Care Devices Based on FET, Electrochemical, and Capacitive Sensing
- DePerio: Deep Learning–Based Early Periodontal Disease Detection
BioSA Publications
- Ghafar-Zadeh, E., Forouhi, S., Osouli Tabrizi, H., Panahi, A., Tahernezhad, Y., & Amrollahi Biyouki, A. (2026). Complementary metal-oxide-semiconductor (CMOS) time-of-evaporation measurement system for binary chemical monitoring. Scientific Reports.
- Sheikhzadeh, A., Moshiri, B., & Ghafar-Zadeh, E. (2026). Dynamic temporal fusion graph neural network for spatio-temporal air quality inference. Engineering Applications of Artificial Intelligence, 167, 113875.
- Amrollahi Biyouki, M., Soheili, F., Delfan, N., Masoudifar, N., Mohaghegh, N., Ebrahimi, S., Daneshvar, S. M. M. H., et al. (2025). DePerio: Innovative deep learning-based framework for periodontal disease diagnosis and severity evaluation using saliva samples. IEEE Journal of Biomedical and Health Informatics.
- Forouhi, S., Osouli Tabrizi, H., Panahi, A., Tahernezhad, Y., & Ghafar-Zadeh, E. (2025). A CMOS time-of-evaporation measurement technique for binary chemical solvent monitoring. IEEE Sensors Journal.
- Soheili, F., Masoudifar, N., Ebrahimi, S., Mohaghegh, N., Daneshvar, M. S. M. H., Amrollahi Biyouki, M., Tahernezhad, Y., Sun, C., Glogauer, M., & Ghafar-Zadeh, E. (2025). DePerio: Deep learning-based oral inflammatory load quantification for periodontal applications. Advanced Intelligent Systems, 7(12), e202500357.
See the full publications list: https://biosa-lab.ca/publications