István Csabai professor of physics, corresponding member of Hungarian Academy of Sciences. He is doing research in several multidisciplinary fields where the new technologies make possible to collect and analyze large amount of data. He started to work on artificial neural networks and their application in the early ’90s. His research focus is to understand complex systems, be it the living cell, the manmade Internet, or the large scale structure of the Universe. Beyond the standard domain knowledge of the traditional disciplines, methods of modern statistical analysis, data mining, machine learning and other computational techniques gain bigger and bigger role. Prof. Csabai has solid background knowledge in these topics and has experience in multidisciplinary research working together with international researchers. He participated as PI or coPI in several national and EU projects, he author of over 200 international publications that received over 70 000 citations
Ribli, D., Horváth, A., Unger, Z., Pollner, P. and Csabai, I., 2018. Detecting and classifying lesions in mammograms with deep learning. Scientific reports, 8(1), pp.1-7. (192 Google Scholar citations in 2 years)
Ribli, D., Pataki, B.Á. and Csabai, I., 2019. An improved cosmological parameter inference scheme motivated by deep learning. Nature Astronomy, 3(1), pp.93-98.
Ladunga, I., Czako, F., Csabai, I. and Geszti, T., 1991. Improving signal peptide prediction accuracy by simulated neural network. Bioinformatics, 7(4), pp.485-487.
Csabai, I., Czako, F. and Fodor, Z., 1991. Quark-and gluon-jet separation using neural networks. Physical Review D, 44(7), p.R1905.
