Dávid is an expert of data science and network theory, and a specialist of diverse web-related solutions. He earned his Ph.D. in Network and Data Science from the Central European University in 2020. He also holds an M.Sc. degree in Computational Physics and a B.Sc. in Physics. Dávid’s main scientific focus is the study and modeling of complex systems. He wrote his doctoral dissertation about the regulatory network mechanics giving rise to the cell division cycle. He has multiple publications in high impact journals emerging from collaborations with institutions such as BIDMC – Harvard Medical School, Penn State University, Central European University, Babes-Bolyai University and The College of Wooster. Dávid also has profound technical experience in web related technologies, such as web interfaces and automating data collection technologies such as web scraping, API usage as well as data cleaning and integration.
David’s major skills are modeling and simulation of complex systems, computational analysis of massive datasets, automated data collection, handling multi-million node complex networks, applying methodologies from statistics and machine learning.
New Publication Co-Authored by Dávid Deritei
Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks – featured on the top journal Science Advances