Ayşe Başar
Director of Data Science Lab
Professor
Program Director of Data Science Masters Program
Faculty of Engineering and Architectural Science
Department of Mechanical and Industrial Engineering
Toronto, Ontario
ayse.bener@torontomu.ca
Office:
(416) 979-5000 ext. 553155
Expert In
Advancing machine learning optimization
Big data and decision making
Big data and information
Deep reinforcement learning
E-waste management
Green IT
Intelligent decision making software models
Intelligent oracles in software engineering
Machine learning
Machine learning explainability
Predictive models
Privacy, security and maintenance
Recommender systems
Software analytics
Technology and intelligent systems
Telecommunications and healthcare
Theme: Transformative technology
Bio/Research
What does it take to train a machine to think? If you are Ayşe Başar, it isn’t just a relentless drive, years of research into AI and machine learning, and more than a decade of experience working in industry. Building predictive models also relies on an ability to step into someone’s shoes. “To ...
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Bio/Research
What does it take to train a machine to think? If you are Ayşe Başar, it isn’t just a relentless drive, years of research into AI and machine learning, and more than a decade of experience working in industry. Building predictive models also relies on an ability to step into someone’s shoes. “To translate what an expert does into mathematics and code, we basically role-play their experiences to understand how they think,” she says.
One such instance came while Başar and her team were building an AI system intended to improve the capacity for emergency room teams at St. Michael’s Hospital. The team’s goal was to assess whether a patient should be discharged or stay in hospital. Once a preliminary model was in place, Başar’s team spent a morning in the ER being trained alongside a group of medical students, ordering tests, looking at patient histories and talking with a patient recovering from surgery. “Being immersed in their world was fascinating and incredibly helpful in modifying our models to think more effectively,” she says.
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One such instance came while Başar and her team were building an AI system intended to improve the capacity for emergency room teams at St. Michael’s Hospital. The team’s goal was to assess whether a patient should be discharged or stay in hospital. Once a preliminary model was in place, Başar’s team spent a morning in the ER being trained alongside a group of medical students, ordering tests, looking at patient histories and talking with a patient recovering from surgery. “Being immersed in their world was fascinating and incredibly helpful in modifying our models to think more effectively,” she says.
Click to Shrink <<