Active Learning Methods for Model Personalization in Mobile Health
In mobile health (mHealth), collecting momentary activity or behavioral state labels often comes at a significantly higher cost or level of user burden than collecting unlabeled data using passive sensing. This observation motivates the idea of attempting to optimize the collection of labeled data to minimize cost or burden when developing personalized models. In this talk, I will present on-going research on the problem of developing active learning methods for use in mobile health that leverage the affordances of this domain while respecting its unique constraints. I will also discuss the need to integrate model personalization and intervention adaptation to support the delivery of just-in-time adaptive interventions (JITAIs).
I am an associate professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst where I co-direct the Machine Learning for Data Science Lab with Brendan O’Connor and Dan Sheldon. I was previously a fellow of both the Pacific Institute for the Mathematical Sciences and the Killam Trusts at the University of British Columbia where I was based in the Laboratory for Computational Intelligence in the Department of Computer Science. I completed my PhD in machine learning in the Department of Computer Science at the University of Toronto.