Prof. Vaclav Snaśel – VSB -Technical University of Ostrava, Czech Republic

Vaclav Snasel’s research and development experience include over 35 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, bioinformatics, information retrieval, knowledge management, data compression, machine intelligence, neural network, nature and biologically inspired computing, data mining, and applied to various real-world problems. Studied numerical mathematics at Palacky University in Olomouc, Ph.D. degree obtained at Masaryk University in Brno, he teaches as a professor at VSB – Technical University of Ostrava. From 2001 to 2009 he worked as a researcher at The Institute of Computer Science of Academy of Sciences of the Czech Republic. Since 2009 he works as head of research program Knowledge management at IT4Innovation National Supercomputing Center, from 2010 until 2017 he worked as dean of the Faculty of Electrical Engineering and Computer Science, and from 2017 he is rector of VSB-Technical University of Ostrava.

He has given 17 plenary lectures and conference tutorials in these areas. He has authored/co-authored several refereed journal/conference papers and book chapters. He has published more than 600 papers (430 papers are indexed at Web of Science, 660 indexed at Scopus). He has

Title: Deep learning for massive data analysis

Recent research trends in the areas of computational intelligence, communications, data mining, and computational models aim to achieve a multi-disciplinary balance between research advances in the fields of collective intelligence, data science, human-centric computing, knowledge management, and network science. The purpose of the lecture is to give perspective, challenges, and opportunities of application Deep learning approach. Deep learning discovers an intricate structure in large data sets by using complex or compressed attributes. These methods have dramatically improved the state-of-the-art in visual object recognition, object detection, network science, and many other domains such as drug discovery and genomics. This talk will discuss some unifying approach for text, image, and network data. Then, this talk will provide some real-life Deep learning applications.