Short Bio

Dr. Laleh Seyyed-Kalantari is an Assistant Professor at York University’s Lassonde School of Engineering and a faculty affiliate at the Vector Institute. She earned her Ph.D. in Electrical Engineering from McMaster University and completed her NSERC Postdoctoral Fellowship (2019–2022) at the Vector Institute and the University of Toronto. Her research centers on AI safety, risk, bias, and interpretability, aiming to mitigate bias and prevent digital exclusion across cultural, social, and healthcare domains. At her Responsible AI Lab, she leverages generative AI and foundation models with adaptive design strategies to improve model performance for underrepresented groups and promote equitable access, representation, and cultural inclusion in AI systems. While her work often applies to health and societal contexts, her broader goal is to develop AI systems that embed cultural and ethical awareness, ensuring technology serves diverse communities responsibly. Dr. Seyyed-Kalantari also contributes to policy efforts through the AI Insights for Policymakers Program, initiated by CIFAR and Mila – Quebec Artificial Intelligence Institute (2024–2025). Her accolades include the Google Research Scholar Award (2024), two York University Research Awards (2025) for Outstanding Early Career and Significant Knowledge Mobilization & Impact, NSERC Postdoctoral Fellowship (2018) and the Banting Postdoctoral Fellowship (2022, declined). Her pioneering work on fairness in medical imaging has been featured in several prominent technology 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)
• York University Research Award for Outstanding Early Career (2025)
• York University Research Award for Significant Knowledge Mobilization & Impact (2025)
• Distinguished Vision Award at ACM KDD Conference Health Day (2025)
• Banting Postdoctoral Fellowship to join Massachusetts Institute of Technology (MIT) University (National, 2022-2024, declined)
• NSERC Postdoctoral Fellowship (National, 2018-2020)

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.