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Manuele Leonelli
Manuele Leonelli
Manuele Leonelli is an Associate Professor of Statistics in the School of Science and Technology at IE University. His research focuses on Bayesian networks and staged tree models for decision-making under uncertainty, with contributions to sensitivity analysis and extreme-value modelling; he is committed to open-source software and reproducible research. He has published in academic journals and international conferences and is Co-Principal Investigator of a Spanish State Research Agency Knowledge Generation Project on semi-parametric models for survival and extremes. He is Editor-in-Chief of ISI Magazine and previously served as Editor of Bernoulli News (2019–2023). He earned a PhD in Statistics at the University of Warwick, completed a postdoctoral fellowship at the Federal University of Rio de Janeiro, served as a Visiting Professor at McGill University, and was a Lecturer in Statistics at the University of Glasgow. His recognitions include the John Copas PhD Thesis Prize and IE University’s SAMBD Best Professor Award (2024). He is a member of the ELLIS Society and author of the forthcoming book Staged Tree Models: With Applications in R (CRC Press).
ACADEMIC EXPERIENCE
• Associate Professor, School of Science and Technology, IE University (Spain), 2025 – Present
• Assistant Professor, School of Science and Technology, IE University (Spain), 2019 – 2025
• Visiting Professor, McGill University (Canada), 2019
• Lecturer in Statistics, University of Glasgow (UK), 2017 – 2019
• Postdoctoral Fellow (CAPES), Federal University of Rio de Janeiro (Brazil), 2015 – 2016
ACADEMIC BACKGROUND
• Postgraduate Certificate in Academic Practice, University of Glasgow (UK), 2018
• PhD in Statistics, University of Warwick (UK), 2015
• MSc in Statistics, University of Warwick (UK), 2011
• BSc in Mathematical Statistics and Data Management, University of Genoa (Italy), 2010
SUSTAINABLE DEVELOPMENT GOALS (SDGs)
3 — Good Health and Well-Being
9 — Industry, Innovation and Infrastructure
13 — Climate Action