I graduated from the Eötvös Loránd Science University in Budapest with an MSc in Physics specialized in Statistical Physics and Complex Systems. To broaden my knowledge in the fields of cognitive and neuroscience I participated in the Berkeley summer course in mining and modelling neuroscience data and the CAJAL computational neuroscience summer course (Lisbon).

I’m interested in understanding what the computational mechanisms are that allow humans and animals to efficiently process the inherently ambiguous and noisy sensory information, and how these computations are implemented in the brain. I’m using probabilistic Bayesian models to approach these questions on the different levels of analyzes. On the computational level, I work on models to describe how do humans integrate uncertain information from sequential observations in an environment with unstable statistical properties. On the algorithmic level, I’m investigating efficient algorithmic realizations of the abstract Bayesian computations that can account for various top-down effects observed in visual cortex. More specifically, I’m testing task-dependent sampling strategies that are optimized to represent complex posterior distributions for resource constrained inference systems.

Qualifications:

BSc: Physics, Budapest University of Technology and Economics (Budapest, 2010)

MSc: Physics, Eotvos Lorand University (Budapest, 2013)