Sana Tonekaboni
Postdoctoral Fellow at Broad Institute of MIT and Harvard
Canada
Summary
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Senior🎓
Top SchoolSana Tonekaboni is a Postdoctoral Fellow at the Broad Institute with a PhD in Computer Science from the University of Toronto and eight years of experience at the intersection of machine learning and healthcare. Her research focuses on self-supervised and multimodal representation learning, explainability, and extracting actionable clinical insights from complex health data, informed by long-term collaborations with hospitals and research institutes. She has blended academic depth with industry exposure through research roles at Vector Institute, Google, and an internship at Apple, translating novel methods into clinically relevant applications. Sana’s background in electrical engineering and bioengineering underpins her work on biomedical signal analysis and early seizure detection, reflecting a practical understanding of physiological data. Colleagues describe her as someone who bridges rigorous theory and applied healthcare impact, often pursuing underexplored multimodal approaches that improve interpretability in clinical settings.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of TorontoBachelor's Degree, Electrical Engineering, Bachelor's Degree, Electrical Engineering at University of Tehran
English, French, Persian, SpanishProgramming languages (2)
Github contributions (5)
sanatonek/TNC_representation_learning
Mar 2020 - Mar 2022
Contributions:54 commits, 3 pushes, 1 comment in 1 year 11 months
sanatonek/time_series_explainability
Feb 2019 - Feb 2021
Contributions:173 commits, 1 push in 2 years
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