My research area is the application of machine learning methods to cognitive neuroscience problems.
More specifically, I work with functional magnetic resonance imaging (fMRI) data trying to
answer these broad questions:
Can we train classifiers to decode a variable of interest, such as what
a subject is seeing, thinking or what decision she is about to make, from
images acquired at the time the thoughts took place?
Can we use successful classifiers to tell us what, in the spatiotemporal
pattern of activation, is connected to that variable and how it is connected?
How can we bring existing knowledge in cognitive neuroscience to bear as constraints
when learning classifiers, so that they learn models of the data that are useful
to someone generating or testing hypotheses about the cognitive process being studied?
To see more detail about any of these, publications or presentations,
please go to the research page.