Mauro Castelli

Associate professor - The NOVA Information Management School , Portugal

Mauro Castelli is an associate professor (with aggregation) and a senior research scientist in the field of Computational Intelligence and Machine Learning in the NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa (Portugal). He received his Ph.D. in Computer Science at the University of Milano Bicocca (Italy). His research interests are focused on the study of machine learning methods that can be used to take advantage of the vast amount of data that are produced nowadays. In particular, the research focuses on the development, implementation, and application of computational intelligence systems for addressing complex real-world problems in different domains.
Dr. Castelli has a proven record of outstanding research, as evidenced by existing high-quality publications. He has published his research in a variety of top-quality academic outlets, such as Expert Systems With Applications, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Applied Soft Computing, Swarm and Evolutionary Computation, Information Systems Frontiers, among others. Mauro has published more than 120 contributions in the area of machine learning and big data in international conferences and journals.
His pedagogical work includes lectures, seminars, and organization of activities at all levels of programs at the home institution and abroad (Italy, Slovenia, Hungary, Japan). He was responsible for lecturing courses on Big Data technologies, Deep Learning, Computational Intelligence, and many others.

He is also the local coordinator of two Erasmus+ Strategic Partnership involving five European universities and focused on the topic of digital transformation and big data technologies.
Dr. Castelli has proved the ability to transfer theoretical insights into practical implications, as demonstrated by his participation in projects with ministers and public administrations including the Directorate General for Education and the Directorate General for Economic Activities among the others. He supervised more than 70 master students and two PhD students. He participated in nine research projects as PI or responsible for several work packages.

Courses By Speaker

Big Data in Official Statistics - Basic Intermediate

Big data is a term used to refer to the study and applications of data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts: volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value. Lately, the term "big data" has been used to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.