Navigating Complexity: Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights

Paula Brito is a Professor at the Faculty of Economics of the University of Porto and Researcher at INESC TEC, Portugal. She holds a PhD in applied mathematics from Paris Dauphine University and a habilitation in applied mathematics from the University of Porto. Her research focuses on multidimensional complex data, known as symbolic data, for which she develops statistical models and multivariate analysis methodologies.

Francesco Denti is an Assistant Professor at the Department of Statistics, University of Padua. Italy. He previously held positions at Università Cattolica del Sacro Cuore in Milan and the University of California, Irvine. He is interested in Bayesian mixtures, Bayesian nonparametrics, model-based clustering, shrinkage priors, and dimensionality reduction.

Francesca Greselin is Professor of Statistics at the University of Milano-Bicocca. Her research focuses on mixture models for classification and clustering, with particular emphasis on robust methods for data analysis. Her work spans computational statistics, and the development of new inferential methods for the study of economic inequality. She is Past President of CLADAG-SIS and serves as Associate Editor of Statistics and Computing.

Krzysztof Jajuga is a Professor of Finance at Wrocław University of Economics and Business, Poland. He holds a doctoral and habilitation degree from Wrocław University of Economics and Business and the titular professor given by the President of Poland. He is the President of the International Federation of Classification Societies (IFCS). He has authored papers and monographs in financial markets, risk management, data analysis, artificial intelligence, multivariate statistics, econometrics and real estate in Polish and English.

Mariangela Zenga is an Associate Professor in Social Statistics at the University of Milano-Bicocca, Italy. Her research interests are in models to study the flows of patients in hospitals, in the gender gap in higher education and in labour market studies.