I use neuroscience to understand and build intelligent learning machines.
Research Scientist @ MIT
The Neuroscience of Learning Machines
I am pioneering the use of neuroscience to understand learning machines, ie. articulating hypothesis and testing them as if learning machines were another brain. Currently, I am developing a theory that facilitates addressing the ongoing crisis in AI regarding lack of interpretability (ie. the "black-box problem") and data inefficiency (ie. requiring large amounts of training data, lack of robustness and poor generalization outside the training distribution).
My research lives at the intersection of engineering of learning machines, theoretical machine learning and neuroscience.
I am grateful to have received training both in machine learning and neuroscience as a postdoc at MIT in the Sinha's lab and Poggio's lab, as well as the multidisciplinary NSF Center for Brains, Minds and Machines. I obtained a doctorate from ETH Zurich (2014) in computer vision and completed a postdoc at the National University of Singapore (2015).