Decision making

Our group focuses on probabilistic machine and human learning. We are interested in smart probabilistic algorithms, as implemented by brains and machines, that are robust and sample-efficient. Our research is roughly divided in two complementary goals that inform each other: (1) We develop new "smart" machine learning methods, in particular for approximate Bayesian inference; (2) We study human probabilistic inference and decision making by computational modeling of psychophysical experiments.

My laboratory is primarily interested in recording and manipulating neural circuits involved in organisms’ ability to adapt to an unpredictable environment. We focus on interactions between cognitive circuits (anterior cingulate cortex, ACC) and action selection and control circuits (motor cortex, basal ganglia), which occur in the context of neuromodulation by the noradrenergic locus coeruleus (LC).