Sana
Tonekaboni

EWSC Postdoctoral Fellow at the Broad Institute of MIT and Harvard

About Me

I'm an EWSC Postdoctoral fellow at the Broad institute of MIT and Harvard. I received my PhD in Computer Science from the University of Toronto, where I worked in close collaboration with clinicians at the Hospital for Sick Children. My research is in machine learning in healthcare and I focus on developing machine learning methods for extracting actionable insights about human health from time series data. My research covers topics in explainable AI, self-supervised representation learning for time series. During my PhD, I received the Apple's scholars in AI/ML fellowship as well as the Health System Impact fellowship from the Canadian Institute of Health Research (CIHR).

My CV

Selected publications

  • Time-Varying Correlation Networks for Interpretable Change Point Detection

    Jenny Yu, Tina Behrouzi, Kopal Garg, Anna Goldenberg, Sana Tonekaboni
    symposium of Machine Learning for Health (ML4H), 2023
    [paper]

  • Modeling Personalized Heart Rate Response to Exercise and Environmental Factors with Wearables Data

    Achille Nazaret, Sana Tonekaboni, Gregory Darnell, Shirley You Ren, Guillermo Sapiro & Andrew C. Miller
    npj Digital Medicine, 2023
    [paper]

  • Decoupling Local and Global Representations of Time Series

    Sana Tonekaboni, Chun-Liang Li, Sercan O Arik, Anna Goldenberg, Tomas Pfister
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    [paper][code]

  • How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at the ICU

    Sana Tonekaboni, Gabriela Morgenshtern, Azadeh Assadi, Aslesha Pokhrel, Xi Huang, Anand Jayarajan, Robert Greer, Gennady Pekhimenko, Melissa McCradden, Fanny Chevalier, Mjaye Mazwi, Anna Goldenberg
    Conference on Health, Inference, and learning (CHIL), 2022.

  • Learning Unsupervised Representations for ICU Timeseries

    Addison Weatherhead, Robert Greer, Michael-Alice Moga, Mjaye Mazwi, Danny Eytan, Anna Goldenberg, Sana Tonekaboni
    Conference on Health, Inference, and learning (CHIL), 2022.

  • Unsupervised Representation Learning for TimeSeries with Temporal Neighborhood Coding

    Sana Tonekaboni, Danny Eytan, Anna Goldenberg
    International Conference on Learning Representations (ICLR), 2020.
    [paper][code]

  • What went wrong and when? Instance-wise feature importance for time-series models

    Sana Tonekaboni*, Shalmali Joshi*, Kieran Campbell, David Duvenaud, Anna Goldenberg
    Neural Information Processing Systems (NeurIPS), 2020.
    [paper] [code][poster]

  • What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

    Sana Tonekaboni*, Shalmali Joshi*, Melissa McCradden, Anna Goldenberg
    Conference on Machine Learning for Healthcare (MLHC), 2019.
    [paper] [poster]

  • Prediction of Cardiac Arrest from Physiological Signals in the PediatricICU

    Sana Tonekaboni, Mjaye Mazwi, Peter Laussen, Danny Eytan, Robert Greer, Sebastian D. Goodfellow, Andrew Goodwin, Michael Brudno, Anna Goldenberg
    Conference on Machine Learning for Healthcare (MLHC), 2018.
    [paper]

  • Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment

    Hossein Kassiri, Sana Tonekaboni, M. Tariqus Salam, Nima Soltani, Karim Abdelhalim, Jose Luis Perez Velazquez, Roman Genov
    IEEE transactions on biomedical circuits and systems 11, 2017.
    [paper]

    • Awards

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