Tomas Pevny has graduated from Faculty of Nuclear sciences and Physical Engineering, CTU, Prague, in 2003. From 2004-2008, he was pursuing Ph.D. at Binghamton University, SUNY, USA specializing on Steganalysis. In 2008-2009 he spent a wonderful post-doc year in Grenoble. Since 2009, he is with Faculty of electrical engineering, CTU, at Prague. From 2013-2019, he was also consulting scientist at Cisco and from 2019 he is consulting scientist at Avast. His specialization is machine learning in security domains. He is an active user of Julia since 2015.
Learning from raw data input is one of the key components of many successful applications of machine learning methods. While machine learning problems are often formulated on data that naturally translate into a vector representation suitable for classifiers, there are data sources with a unifying hierarchical structure, such as JSON. This talk will describe Mill.jl and JsonGrinder.jl, which offers a theoretically justified approach to solve machine learning problems with these data sources.