Short Bio

Dr. Laleh Seyyed-Kalantari is an Assistant Professor of Computer Science in the Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University. Before that, Dr. Seyyed-Kalantari was an Associate Scientist at Lunenfeld Tanenbaum Research Institute (Research Institute of Mont Sinai Hospital), Toronto, Canada, where she was working on research projects for medical image segmentation in prostate cancer, kidney cancer as well as prognostication in kidney cancer using 3D volumetric CT and MRI images. With a Ph.D. in electrical engineering from McMaster University (2017), she was an NSERC postdoctoral fellow at the Vector Institute for Artificial Intelligence and the University of Toronto (2019-2022). Her research, under the supervision of Dr. Marzyeh Ghassemi , has been focused on developing Artificial intelligence (AI) diagnostic tools with a focus on their fairness. Her research in demonstrating unfairness in AI-based medical image diagnostic was featured in several international technical news such as Daily Mail, Venture Beat, UNILAD, Nature Medicine news & Views, National Post, The Boston Globe, KMGH-TV (channel 7 a television station in Denver), etc. Her ultimate goal is to remove barriers toward the trustable deployment of AI diagnostic tools in clinics, such that they benefit the patients, provide fairness, and reduce the workload of clinical staff. She has received a number of highly competitive national, provincial, and institutional awards such as the Banting Postdoctoral Fellowship (2022-declined), and NSERC Postdoctoral Fellowship (2018), among others.

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)

Selected talks

• ‘AI in Healthcare: Risks of Race Detection in Medical Imaging’, Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI conference, invited panel speaker, Washington DC, NY, April 2022.
• ‘We’re (not) fine: Lack of fairness in AI-based medical image diagnostic tools’, AI for Healthcare Conference – for West African French-speaking countries (Benin, Burkina Faso, Côte, D'Ivoire, Guinea, Mali, Niger, Senegal, and Togo), African Institute of Business and Technology, invited talk, virtual, May 2022.
• ‘We’re (not) fine: Lack of fairness in AI-based medical image diagnostic tools’, Toronto Machine Learning Summit, invited talk, virtual, April 2022.
• ‘Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations’, Stanford AIMI Journal Club, invited talk, virtual, Feb. 2022.
• ‘AI in medical imaging, applications and challenges’, McMaster University, invited lecture guest speaker, virtual Feb. 2022.
• ‘Opportunities and barriers toward deployment of medical image classifiers’, Vector Institute in Artificial Intelligence, March. 2021.
• ‘Fairness gap in deep learning medical image classifiers’, WinAAA Meetup NeurIPS 2020, Virtual, Dec. 2020.
• ‘“Super-efficient gradient estimation technique,’’ Recent advances in efficient adjoint sensitivity analysis and its application in metamaterial design’, Design of Acoustics Metamaterials: Optimization and Machine Learning II, 178th Meeting of the Acoustical Society of America, invited talk, San Diego, CA, Dec. 2019.