Vincent Gaudet, PEng

Vincent Gaudet is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo.

His research interests focus on high-speed and energy-efficient microelectronic circuits applied to digital communication systems and signal processing. He is particularly interested in stochastic computing systems and multiple-valued logic. He has worked on low-density parity-check (LDPC) and turbo decoders, multiple-access channels, implantable neural recording systems, and integrated lab-on-chip instrumentation circuitry.

Dr. Gaudet is a Senior Member of the IEEE, and holds many editorial positions. In 2009, he received the Petro Canada Young Innovator Award to recognize and support his work of outstanding young faculty-based research.

• VLSI circuits, Analog circuits, Mixed-signal circuits, Digital circuits, CMOS design, Low-power circuits, LDPC decoding, Turbo decoding, Iterative decoding, Stochastic computation, Connectivity and Internet of Things, Digital design and fabrication technologies, Embedded systems, Information systems, Sensors and devices, Wireless communications/networking, Microelectronic circuits, Digital communications, signal processing, Connectivity and Internet of Things, IoT, Devices,

• 2003, Doctorate Electrical Engineering, University of Toronto, Canada
• 1997, Master of Applied Science Electrical Engineering, University of Toronto, Canada

• ECE 637 - Digital Integrated Circuits
  • Taught in 2024
• MTE 421 - Linear and Nonlinear Electronics
  • Taught in 2024

* Only courses taught in the past 5 years are displayed.

• Zukotynski K.A., Gaudet V.C., Uribe C.F., Chiam K., Benard F., and Gerbaudo V.H., Clinical Applications of Artificial Intelligence in Positron Emission Tomography of Lung Cancer, PET Clinics, Volume 17, 77-84, Canada, 2022.
• Srivastava R., Gaudet V.C., and Mitran P., Hardware Implementation of a Fixed-Point Decoder for Low-Density Lattice Codes, Journal of Signal Processing Systems, Volume 94, 101-116, Canada, 2022.
• Chiam K., Lee L., Kuo P.H., Gaudet V.C., Black S.E., and Zukotynski K.A., Brain PET and Cerebrovascular Disease, PET Clinics, Volume 18, 115-122, Canada, 2023.
• Poorheravi P.A., and Gaudet V., FPGA-Based Architectures for Random Forest Acceleration, Midwest Symposium on Circuits and Systems, Volume 127, Canada, 2022.
• Ottoy J., Ozzoude M., Zukotynski K., Adamo S., Scott C., Gaudet V., Ramirez J., Swardfager W., Cogo-Moreira H., Lam B., Bhan A., Mojiri P., Kang M.S., Rabin J.S., Kiss A., Strother S., Bocti C., Borrie M., Chertkow H., Frayne R., Hsiung R., Laforce R.J., Noseworthy M.D., Prato F.S., Sahlas D.J., Smith E.E., Kuo P.H., Sossi V., Thiel A., Soucy J.-P., Tardif J.-C., Black S.E., and Goubran M., Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET, Alzheimer's and Dementia, Volume 19, 1503-1517, Canada, 2022.
• Zukotynski K., Gaudet V., Uribe C.F., Mathotaarachchi S., Smith K.C., Rosa-Neto P., Benard F., and Black S.E., Machine learning in nuclear medicine: Part 2-neural networks and clinical aspects, Journal of Nuclear Medicine, Volume 62, 22-29, Canada, 2021.
• Zukotynski K., Black S.E., Kuo P.H., Bhan A., Adamo S., Scott C.J.M., Lam B., Masellis M., Kumar S., Fischer C.E., Tartaglia M.C., Lang A.E., Tang-Wai D.F., Freedman M., Vasdev N., and Gaudet V., Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative, Clinical Nuclear Medicine, Volume 46, 616-620, Canada, 2021.
• Poorheravi P.A., and Gaudet V., Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets, Proceedings of The International Symposium on Multiple-Valued Logic, Volume 127, 136-141, Canada, 2021.
• Brosch-Lenz J., Yousefirizi F., Zukotynski K., Beauregard J.-M., Gaudet V., Saboury B., Rahmim A., and Uribe C., Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies, PET Clinics, Volume 16, 627-641, Canada, 2021.
• Saad M., Chaudhary M., Nassar L., Karray F., and Gaudet V., Versatile Deep Learning Based Application for Time Series Imputation, Proceedings of the International Joint Conference on Neural Networks, Volume 127, Canada, 2021.