Human-centred adaptive systems
How can adaptive and affect-aware systems support learning and wellbeing without overreading sparse, intimate signals?
Systems that adapt to people need to be honest about how little they know about the person in front of them.
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.
Selected work
- PolicyAI in health: how smart technology is breaking new frontiers in medical careAuthor, Digital Skills and Jobs Platform, European Commission2026
- PaperData preprocessing and machine learning in wearable data analysis: assessing efficacy and challenges for mental health monitoring of healthcare professionalsAuthor, Université Paris Cité2024
- PaperThe Alternate Education for the 21st CenturyCo-author, 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)2019
- ReportCHI, a knowledge-sharing digital platform powered by machine learningAuthor, UNESCO MGIEP2019
Research projects
Wearable data and clinician wellbeing
Doctoral research on whether wearable sensor data can support mental-health risk monitoring for frontline healthcare professionals, and where the method breaks down.
Next / Territory
Data governance for public-interest systems