Dávid studies how our minds process the vast visual stimuli and represent the complex casual relationships of our environment. His research is done in close collaboration with the VisonLab of the CEU Department of Cognitive Science. The concrete question is how depth is inferred and represented in our brain from a practically two dimensional input. The study involves a probabilistic framework of bleeding edge machine learning technologies and Bayesian networks.
Dávid is a physicist by training and had previous collaborations with Babes Bolyai University, Beth Israel Deaconess Medical Center - Harvard Medical School, University of Notre Dame and the College of Wooster. He is also a part-time colleague of the Basel based company Akceso Advisors A.G.
Deritei, Dávid, et al. "Community detection by graph Voronoi diagrams." New Journal of Physics 16.6 (2014): 063007.
Deritei, Dávid, et al. "Principles of dynamical modularity in biological regulatory networks." Scientific reports 6 (2016).
Lázár, Z. I., Papp, I., Varga, L., Járai-Szabó, F., Deritei, D., & Ercsey-Ravasz, M. (2017). Stochastic graph Voronoi tessellation reveals community structure. Physical Review E, 95(2), 022306.