Broadly, my research is focused on machine learning and data science applied to neuroscience and neuroengineering. This has led to engagement in several related fields, especially neurotechnology and neuroinformatics. 

My neurotechnology work revolves around facilitating human-machine co-adaptation in brain-computer interfaces. The aim is to improve the ability of a computer to adapt to a human user as they naturally change over time, while at the same time training the user to effectively module their own brain activity. The adaptation of these two processes are unified via neurofeedback that is algorithmically generated with input from both the human user and the machine learning algorithms driving the interface.

My neuroinformatics work borrows heavily from my neurotechnology research. Often this takes the form of reimagining how the computational methods used to make brain-computer interfaces possible can be used extract scientifically or clinically meaningful information from the brain at the individual level, such as attentional states in real-time. 

AI in Health


Find my full list of publications on Google Scholar.

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AI in Health