Luigi Acerbi

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.