Gael Varoquaux

Wednesday 17th January 2018

Time: 4.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

Using machine learning to understand cognition from brain images

Imaging brain activity provides unique data to understand mental
processes. Yet, these data are usual tackled with a strongly reductionist
approach, used merely to test a simple hypothesis from cognitive science.

In this talk, I would like to explore how machine learning can be used to
extract more information from brain activity, and one day refine
cognitive science models from the data. From a neuroscience perspective,
the stakes are to give descriptions of behavior that bridge cognitive
paradigms and to define the prototypical functions implemented by a brain
network. From a machine learning perspective, the challenge are that of
statistics and representation learning in a small-sample regime.

Bio:
Gaël Varoquaux is a tenured computer-science researcher at INRIA. His
research develops statistical learning tools for functional neuroimaging
data with application to mapping brain of cognition and pathologies. In
addition, he is heavily invested in software development for data
science, as project-lead for scikit-learn, one of the reference
machine-learning toolboxes, and on joblib, Mayavi, and nilearn. Varoquaux
has contributed key methods for functional brain atlasing, extracting
brain connectomes, population studies, as well as efficient models for
high-dimensional data-scarce machine learning beyond brain imaging. He
has a PhD in quantum physics and is a graduate from Ecole Normale
Superieure, Paris.