Umberto Michelucci studied physics and mathematics. He is an expert in numerical simulation, statistics, data science and machine learning. He has steadily expanded his expertise in post-graduate courses and research projects over the years. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the areas of data warehouse, data science and machine learning. He is currently responsible for deep learning, new technologies and research collaborations at Helsana Versicherung AG. In 2014, he completed a Postgraduate Certificate in Professional Studies in Education in England to expand his teaching and pedagogy skills. He is the author of “Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks” published by Springer in 2018. He is currently working on a second book on “Convolutional and Recurrent Neural Networks Theory and Applications. He is very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular lectures at international conferences.