Sana Tonekaboni is an EWSC Postdoctoral Fellow at the Broad Institute and the Vector Institute. Her research broadly focuses on machine learning and health and building solutions for barriers of adoption of AI in clinical settings. Her research work focuses on time series, more specifically self-supervised representation and explainability in this setting. She received her PhD in computer science at the University of Toronto. She has been a Health System Impact Fellow of CIHR and an Apple Scholar in AI/ML Fellow during her PHD.