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As a first-year Ph.D. candidate in Artificial Intelligence for health, my research focuses on the crucial aspect of robustness in AI models. My objective is to clearly define the concept of robustness in the context of machine learning-based solutions for healthcare and determine the key priorities for assessing robustness in these solutions. I am also utilizing data collected from actual hospitals to construct predictive models. This unique combination of academic knowledge and real-world application allows me to bridge the gap between academia and real-world application, ultimately driving meaningful change in AI for healthcare.

Research interests

  • Machine learning for Health
  • Trustworthy AI
  • Machine learning robustness



Conceptualizing and assessing the robustness of healthcare algorithms

Alan Balendran

Promotion : 2022

Supervisor.s : Raphaël Porcher

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