Research area
Systems that adapt to people need to be honest about how little they know about the person in front of them.
How can adaptive and affect-aware systems support learning and wellbeing without overreading sparse, intimate signals?
My doctoral research sat here: machine learning on wearable sensor data to monitor the mental health of frontline healthcare workers. The useful finding was as much about limits as about accuracy. Signals from the body are sparse, noisy and deeply contextual, and a model that performs well on a cohort can still be the wrong instrument for an individual decision.
The same tension runs through adaptive learning systems, which I worked on earlier in education settings: personalisation is only as good as the model of the learner, and that model is always thinner than the learner.
Outputs
2026
Digital Skills and Jobs Platform, European Commission
A deep-dive for the European Commission's skills platform on where clinical AI is genuinely useful, and what assurance regulated care requires of it.
PolicyAuthor
2024
Université Paris Cité
Doctoral research on what wearable sensor data can and cannot support when the question is clinical risk.
PublicationsAuthor
2019
2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Early work connecting cognitive science and socio-emotional learning to digital pedagogy.
PublicationsCo-author
2019
UNESCO MGIEP
How a learning platform used machine learning to connect knowledge across a UNESCO institute's work.
ReportsAuthor
Projects
Doctoral research on whether wearable sensor data can support mental-health risk monitoring for frontline healthcare professionals, and where the method breaks down.