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Laleh Seyyed-Kalantari

Assistant Professor

Department:

Electrical Engineering & Computer Science

Bio

Laleh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University. Before that, she was an Associate Scientist at Lunenfeld Tanenbaum Research Institute, Toronto, Canada. With a Ph.D. in electrical engineering from McMaster University (2017), she was an NSERC postdoctoral fellow at the Vector Institute and the University of Toronto (2019-2022). Her research interests are responsible AI and developing AI diagnostic tools that focus on their fairness. She has received several highly competitive national, provincial, and institutional awards, such as Banting Postdoctoral Fellowship (2022-declined) and NSERC Postdoctoral Fellowship (2018). Her research in the area of AI model fairness in medical imaging has been featured in many Tech. News.

Research Interests

  • Responsible AI
  • Fairness of AI model
  • AI in medical imaging
  • Machine learning in healthcare

Selected awards and achievements

  • Banting Postdoctoral Fellowship to join Massachusetts Institute of Technology (MIT) University. (National, 2022-2024, declined )
  • NSERC Postdoctoral Fellowship. (National, 2018-2020)
  • Finalist of the 2021 CIFAR ‘AICan 3-M Impact’ Competition. (National, 2021)
  • Winner team (1st rank) of the Toronto Health Data Hackathon (served as a team lead). (Municipal, 2019)
  • Nominee for NSERC and L’Oréal-UNESCO for Women in Science. (National, 2018)
  • Research in Motion Ontario Graduate Scholarship. (Provincial, 2015)
  • Ontario Graduate Scholarship and Queen Elizabeth II Graduate Scholarship in Science and Technology. (Provincial, 2014-2015)
  • Ontario Graduate Scholarship. (Provincial, 2013-2014)

Featured in Tech. News

Our 'Nature Medicine' paper demonstrating underdiagnosis bias amplification on AI medical image diagnostic tools were featured in the following tech news:

Our 'The Lancet Digital Health' paper on demonstrating AI can detect a patient’s race from the medical image was featured in the following tech news among the others:

Interviewed by the editor-in-chief of The Lancet Digital Health journal for a podcast about our race detection paper.

I was featured as ‘AI Champions’ in Artificial Intelligence in Medicine (AIMED).

Our PSB 2021 conference paper on demonstrating the lack of fairness in disease diagnosis medical image classifiers was featured in: