Artificial intelligence

HiPerCog lab studies, and aims to develop a theory of, High Performance Cognition (HPC), which arises when a highly demanding dynamic cognitive task is performed with high skill. HPC is hypothesized to be due to tuning of attention processes. It generates subjective experience of Flow, and is a component of learning to perform to expert/optimal level. A key tenet of HPC is that it arises through learning of a demanding dynamic task. We work on projects studying skill learning in a range of tasks, including games, programming, and neurofeedback.

The Biological Physics group (about 20 members) focuses on computational biophysics. Its goal is to improve the understanding of biological processes, thus promoting health, and to reveal the molecular-level mechanisms of disease. The focus of the research is, for example, neurological diseases, type 2 diabetes, cardiovascular diseases, cancers and eye diseases, as well as the development of computational methods and data analytics (AI/ML). During the last 5 years, the group has published more than 120 articles (incl.

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.