The research in my lab aims to understand the computational design principles that govern our cognition. These design principles point to concrete mechanistic descriptions of cognition in health and disease, and can
further inform of ways to close the gap between machine and human intelligence. To this aim, we employ experiments with high temporal and/or spatial resolution on many participants ("big data"), and analyze it with a wide range of mathematical tools from dynamical systems, statistical physics, control theory, information theory, functional data analysis, and more... 

We study the underlying mechanisms of diverse cognitive processes - creativity, the social cues we send through our motion and their inference, reasoning mechanisms under noisy perception (such as geometric and physical intuitions), and computational trade-offs in health and disease (e.g. people diagnosed on the autistic spectrum and Parkinson patients).