Soosan Beheshti
Professor
Program Director, Electrical Engineering
Faculty of Engineering and Architectural Science
Department of Electrical, Computer, and Biomedical Engineering
Toronto, Ontario
soosan@torontomu.ca
Office:
(416) 979-5000 ext. 554906
Expert In
Algorithms for applications in data modeling
Clean technologies
Data denoising and compression
Energy and sustainability
Green energy
Signal and information processing
Software tools for solar energy farms
Statistical signal processing and information theory
System modeling and control
Technology and intelligent systems
Theme: Climate, environment and sustainability
Theme: Transformative technology
Bio/Research
Soosan Beheshti received the B.S. degree from Isfahan University of Technology, Isfahan, Iran, and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), Cambridge, in 1996 and 2002 respectively, all in electrical engineering. From September 2002 to June 2005, she was a ...
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Bio/Research
Soosan Beheshti received the B.S. degree from Isfahan University of Technology, Isfahan, Iran, and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), Cambridge, in 1996 and 2002 respectively, all in electrical engineering. From September 2002 to June 2005, she was a Postdoctoral Associate and a Lecturer at MIT. Since July 2005, she has been with the Department of Electrical and Computer Engineering, Toronto Metropolitan University (formerly Ryerson), where she is currently a Professor.
A childhood passion for math, philosophy, electromagnetic waves, and problem-solving led Soosan Beheshti to become an electrical engineer. Her early research and studies on communication systems design piqued her curiosity and led her to consider a range of questions on data modelling for the purpose of prediction and control. This area would become the foundation of Beheshti’s research, and her models can be adapted to a broad number of machine learning applications, from medical imaging to data clustering.
For Beheshti, simpler is better. “That’s modelling,” she says. In her research on statistical signal and data processing, Beheshti harnesses data for parametric modelling. To get to the underlying structure of a set of observed data, she turns to Occam’s razor law of parsimony philosophy, a 14th-century problem-solving principle that argues, “Entities should not be multiplied without necessity.”
“How much we trust the data dictates the complexity of the model structure that we consider,” says Beheshti. As more data is gathered that requires a faster modelling process, her research will continue to focus on meeting the challenges related to model complexity, validity, and reliability.
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A childhood passion for math, philosophy, electromagnetic waves, and problem-solving led Soosan Beheshti to become an electrical engineer. Her early research and studies on communication systems design piqued her curiosity and led her to consider a range of questions on data modelling for the purpose of prediction and control. This area would become the foundation of Beheshti’s research, and her models can be adapted to a broad number of machine learning applications, from medical imaging to data clustering.
For Beheshti, simpler is better. “That’s modelling,” she says. In her research on statistical signal and data processing, Beheshti harnesses data for parametric modelling. To get to the underlying structure of a set of observed data, she turns to Occam’s razor law of parsimony philosophy, a 14th-century problem-solving principle that argues, “Entities should not be multiplied without necessity.”
“How much we trust the data dictates the complexity of the model structure that we consider,” says Beheshti. As more data is gathered that requires a faster modelling process, her research will continue to focus on meeting the challenges related to model complexity, validity, and reliability.
Click to Shrink <<