Broadly, my research is focused on machine learning and data science applied to neuroscience and neuroengineering. More specifically, the main thrust of my research has been in using computational modelling in order to create new approaches to facilitating human-machine co-adaptation in neurofeedback and brain-computer interfacing. These new methods aim to enhance the ability of a human user to learn to effectively modulate their own brain activity by using statistical and machine learning models to algorithmically generate feedback while machine learning models simultaneously learn to interpret the user’s changing brain activity.
I have also collaborated with other groups as a data scientist applying machine learning to genetic analysis.