Laleh Seyyed-Kalantari

Laleh Seyyed-Kalantari is a computer scientist who studies fairness and responsible AI in medical imaging and healthcare. She completed a PhD in Electrical Engineering at McMaster University in 2017 and an NSERC postdoctoral fellowship at the Vector Institute and the University of Toronto from 2019 to 2022. As lead author, she published a 2021 Nature Medicine study that found chest X-ray classifiers consistently underdiagnosed under-served patients, and she co-authored the 2021 Reading Race preprint on AI recognition of race in medical images. Her work earned a Google Research Scholar gift in 2024 and two York University Research Awards in 2025. She is an assistant professor at York University's Lassonde School of Engineering, where she directs the Responsible AI Lab, and a faculty affiliate at the Vector Institute.

Now at: Vector Institute and Lassonde School of Engineering

Timeline

2017

A PhD from McMaster

She earns a PhD in Electrical Engineering from McMaster University in 2017.
2019–2022

Postdoc at Vector and Toronto

From 2019 to 2022, she is an NSERC Postdoctoral Fellow at the Vector Institute and the University of Toronto. During these fellowships she begins investigating inaccuracies in AI diagnostics.
July 2021

The Reading Race preprint

In July 2021, she is one of 23 authors of the arXiv preprint "Reading Race: AI Recognises Patient's Racial Identity In Medical Images". The authors show that standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities.
December 2021

Underdiagnosis bias in Nature Medicine

In December 2021, she publishes a Nature Medicine paper as lead author, with Haoran Zhang, Matthew B. A. McDermott, Irene Y. Chen and Marzyeh Ghassemi. Across three large chest X-ray datasets and one multi-source dataset, the classifiers consistently and selectively underdiagnose under-served patient populations.
By 2023

Assistant professor at York

By 2023, she is an assistant professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering, after a post as Associate Scientist at the Lunenfeld Tanenbaum Research Institute in Toronto. Her multi-institutional team finds that AI models can determine a patient's race from X-ray, CT and mammographic images alone, a result published in The Lancet Digital Health.
2024

A Google Research Scholar gift

In 2024, she receives a monetary gift from Google's Research Scholar Program, which supports early-career professors. Her Responsible AI Lab works on fairness of AI in medical imaging and large language models and on AI applications in drug discovery, and her group organizes the Responsible Language Models workshop (ReLM 2024) at AAAI in Vancouver.
2025

Awards from York and KDD

In 2025, she receives two York University Research Awards, for Outstanding Early Career and for Significant Knowledge Mobilization & Impact, and the Distinguished Vision Award at the ACM KDD Conference Health Day. In November 2025, York notes that 100 per cent of her trainees have secured competitive awards.
As of October 2026

Directing the Responsible AI Lab

She is an assistant professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering, a faculty affiliate at the Vector Institute and director of the Responsible AI Lab. The lab uses generative AI and foundation models to improve model performance for underrepresented groups. From 2024 to 2025, she also contributes to the AI Insights for Policymakers Program, initiated by CIFAR and Mila.