Statistical Modelling and Data Science : An introduction with illustrative examples About the presentationSince the turn of the millenium, technological innovations have allowed the collection and storage of increasingly complex and large data sets, a situation that has in turn spurred significant advances in such domains as medicine, meteorology, renewable energies, engineering, sports, social sciences, and many more. Most of these scientific advances are grounded on strong mathematical foundations - see for instance the variety of epidemiological models required to understand the COVID-19 spread, or the statistical theories underpinning clinical studies. In his talk, Prof. Christophe Ley will provide a friendly overview on statistical modelling and data science, two important themes of his own research and that of his research team. He will describe modern challenges and, in particular, focus on research he wishes to undertake in the coming years.
About the speakerChristophe Ley joined the Department of Mathematics at the University of Luxembourg in November 2021 as Associate Professor. He is an applied mathematician working in the field of statistics/data science, with a strong taste for interdisciplinary work. Before joining the University of Luxembourg, he was Associate Professor at Ghent University in Belgium. He obtained his PhD from the Université libre de Bruxelles in 2010, where he had also studied except for the first year, which he did at the University of Luxembourg during its first year of existence in 2003-2004.
He is President of the Luxembourg Statistical Society, President of the European organisation for Advanced Courses in Statistics (ECAS), and Leader of the international network S-TRAINING (new data science methods for sports analytics and sports medicine). Among his achievements are the Prix Marie-Jeanne Laurent-Duhamel, rewarding the best PhD thesis in Statistics over a period of 3 years among all French-speaking universities world-wide, and an Elected Membership at the International Statistical Institute.
He leads the research team MIDAS which stands for Modelling - Interdisciplinary - Data (Science) - Applied - Statistics. The team’s aim is to develop innovative statistical and machine learning procedures based on new mathematical and computational tools to tackle the challenges posed by modern days’ increasingly complex and large data sets.
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