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.