University of Helsinki

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.

We are implementing computational models into spiking network simulations, with the aim to better understand the relation between cortical computation and visual physiology.