Keynote Speakers

Eva Ceulemans

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Dimitris Karlis

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Regina Liu is Distinguished Professor at Rutgers University, USA. Her research areas include data depth, resampling, confidence distribution, and fusion learning. Aside from theoretical and methodological research, she has long collaborated with the FAA on aviation safety research projects on statistical process control, text mining and risk management. She has served as Co-Editor for the Journal of the American Statistical Association and the Journal of Multivariate Analysis, and as Associate Editor for several journals, including the Annals of Statistics. She is an elected fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected member of the International Statistical Institute. Among other distinctions, she is the recipient of 2021 Noether Distinguished Scholar Award (American Statistical Association), 2011 Stieltjes Professorship (The Netherlands), and has delivered an IMS Medallion Lecture. She was elected President of the Institute of Mathematical Statistics, 2020-2021.

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Giovanni Camillo Porzio

Giovanni Camillo Porzio is full Professor of Statistics at the University of Cassino and Southern Lazio, he holds a Master in Statistics from the University of Minnesota, and a Ph.D. in Computational Statistics and Data Analysis from the University of Naples Federico II. He has worked in many areas of applied statistics research, and made important contributions to the statistical analysis of directional data, in particular from a non-parametric and data depth perspective. By introducing a circular boxplot, his work has contributed to progress in circular data visualization techniques. His recent research has focused on supervised and unsupervised learning methods. He is particularly interested in developing strategies that are robust to the presence of data anomalies.

He is the President Elect of CLADAG (Classification and Data Analysis Group), one of the main Sections of the Italian Statistical Society.

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Peter Rousseeuw

Peter Rousseeuw is known mainly for his work on robust statistics. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the k-medoids clustering method, and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of ISI and a fellow of IMS and ASA. In the course of his career, Peter published three books and over 200 papers on theory, algorithms and applications, together receiving over 115,000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaa, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. Peter’s recent work is mainly on robustness to cellwise outliers.

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