Dr. Soosan Beheshti

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.