Ubiquitous Machines Learning for Design and Implementation of Energy-Efficient Electrical Systems: A Wide Range of Uses and Applications
IEEE Industry Relations Committee (on behalf of IEEE Canada) would like to invite you to attend the Webinar "Ubiquitous Machines Learning for Design and Implementation of Energy-Efficient Electrical Systems: A Wide Range of Uses and Applications"
The objective of this webinar is to discuss how Artificial Intelligence (AI) can play a significant role in several applications in electrical engineering. The webinar intends to provide an update/overview of the recent trends and advances of AI research and to open a dialog on how to address the challenges faced in the design of energy-efficient electrical systems. Several pathways to innovate through electronic systems design will be discussed through a series of ongoing projects.
Date and Time
Location
Hosts
Registration
- Date: 01 Jun 2021
- Time: 01:00 PM to 02:00 PM
- All times are (GMT-05:00) Canada/Eastern
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- Co-sponsored by IEEE Industry Relations Committee Canada
- Starts 11 May 2021 09:00 AM
- Ends 01 June 2021 02:00 PM
- All times are (GMT-05:00) Canada/Eastern
- No Admission Charge
Speakers
Prof. Yvon Savaria
Biography:
Yvon Savaria FIEEE (S' 77, M' 86, SM' 97, F’08) received the B.Ing. and M.Sc.A in electrical engineering from Polytechnique Montreal in 1980 and 1982 respectively. He also received the Ph.D. in electrical engineering in 1985 from McGill University. Since 1985, he has been with Polytechnique Montreal, where he is currently professor in the department of electrical engineering. He is also affiliated with Hangzhou Innovation Institute of Beihang University. He has carried work in several areas related to microelectronic circuits and microsystems such as testing, verification, validation, clocking methods, defect and fault tolerance, effects of radiation on electronics, high-speed interconnects and circuit design techniques, CAD methods, reconfigurable computing and applications of microelectronics to telecommunications, networking, aerospace, image processing, video processing, radar signal processing, and digital signal processing acceleration. He is currently involved in several projects that relate to aircraft embedded systems, asynchronous circuits design and test, virtual networks, software defined networks, machine learning, computational efficiency and application specific architecture design. He holds 16 patents, has published 180 journal papers and 470 conference papers, and he was the thesis advisor of 175 graduate students who completed their studies.
Dr. Ahmed Ragab
Biography:
Ahmed Ragab is an AI research scientist working for CanmetENERGY, an energy innovation center of Natural Resources Canada (NRCan). He received a Ph.D. degree in Industrial Engineering from Polytechnique Montréal in 2014. His research interests include AI, Image Processing, Data Fusion, Causality Analysis, Operations Research, Discrete Event Systems, and Process Mining. He has a bunch of experience in developing advanced algorithms and tools in the manufacturing industry, aiming at reducing energy consumption, Greenhouse gas (GHG) emissions, and operational and maintenance costs while improving operations’ performance. His main thematic activities focus on the practical challenges of Big Data and AI in a number of applications including Abnormal Events Diagnosis & Prognosis, Predictive Maintenance, Supervisory Control, Real-Time Optimization and Systems Design.