Short Bio

Dr. Laleh Seyyed-Kalantari is an Assistant Professor at York University's Lassonde School of Engineering. She conducted postdoctoral research at the Vector Institute and the University of Toronto as an NSERC fellow (2019-2022). She holds a Ph.D. in electrical engineering from McMaster University (2017). Her research interests are responsible AI, generative AI, and AI fairness. Dr. Seyyed-Kalantari has garnered prestigious awards such as Google Research Scholar Program award (2024), Banting Postdoctoral Fellowship (2022-declined) and NSERC Postdoctoral Fellowship (2018), among others. She has received recognition for her contributions to AI model fairness in medical imaging, featured in various tech news outlets.

Research Interests

• Responsible AI
• Generative AI
• Fairness of AI model
• AI in medical imaging
• AI risks

Selected awards and achievements

• Google Research Scholar Program award (2024)
• 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.