Keynote Speakers
Prof.Francesco Bartolucci
University of Perugia, Italy
Since 2007 Francesco Bartolucci is a Full Professor of Statistics in the Department of Economics of the University of Perugia (IT), where he also coordinated the Doctorate program in “Mathematical and Statistical Methods for Economic and Social Sciences”. Among his research interests, hidden Markov models and discrete latent variable models in general have a special role. On these topics he published papers appeared in prestigious journals of Statistics and Econometrics and he coauthored two books with an international publisher. He collaborated with many young researchers even within graduate programs and acted as Reviewer and Associate Editor for important field journals. He also acted as a reviewer within evaluation programs of the Italian University system. He participated in several research programs, even with the role of coordinator. He was the Principal Investigator of the research project “Mixture and latent variable models for causal inference and analysis of socio-economic data”, which was funded by the Italian Government. He is an Editor of Statistical Modelling: An International Journal.
Speech Title：Discrete latent variable models with a special focus on hidden Markov models
Abstract：Models based on the assumption that the response variables can be explained on the basis of discrete unobservable (latent) variables will be illustrated. These models have many potentialities, among which it is worth mentioning the possibility of performing model-based clustering and accounting for unobserved heterogeneity in a flexible way. Apart from an illustration of the main assumptions and of the most recent extensions to deal with complex data structures, the focus will be on estimation methods, model selection, and prediction. Local and global decoding will be also discussed. Special attention will be given to the latent class, hidden Markov, and stochastic block models. The illustration will be based on applications in different fields and considering the need of scalability with large datasets. This is a joint work with Silvia Pandolfi (Univeristy of Perugia, IT) and FUlvia Pennoni (University of Milano-Bicocca, IT).
Keywords：Model-based clustering; data augmentation; EM algorithm; latent class model; local and global decoding; stochastic block model
Prof.Paulo Canas Rodrigues
Federal University of Bahia, Brazil
Paulo Canas Rodrigues obtained his PhD in Statistics at Universidade Nova de Lisboa (2012), and his Aggregation (Habilitation) in Mathematics, specialization in Statistics and Stochastic Processes, at Universidade de Lisboa (2019). He is currently a professor in the Statistics Department at the Federal University of Bahia (UFBA). He has published more than 70 scientific papers, in collaboration with more than 90 co-authors from 20 countries and has extensive experience in editorial activities. Paulo was Vice-President of the International Association for Business and Industrial Statistics between 2009 and 2013 and was the founder and Chairman of the Latin American Regional Section of the International Association for Statistical Computing between 2017 and 2019. Among other activities, he is currently the President of the Brazilian Region of the International Biometric Society, Member of the Representative Council of the International Biometric Society, Principal Investigator of the Statistical Learning Laboratory, Chair of the Special Interest Group on Data Science of the International Statistical Institute, and President-Elect of the International Association for Business and Industrial Statistics.
Speech Title：Statistics and Data Science: Transforming data into information
Abstract：In recent years, immense amounts of data have been generated, from sensors to purchase transaction records, mobile GPS signals, digital satellite images and social media. The recent fourth industrial revolution brought even bigger amounts of data from the internet of things application. The raise of data collection has brought the need for quantitative minded professionals able to transform that data into information and decision making. In this talk I will give a general overview about how statistics and data science are extremely important in every discipline and in everyday life. I will also present some big data applications and will discuss the usefulness of statistics in the Era of internet of things and artificial intelligence.
Keywords：Data science, Internet of things, Artificial inteligence