Bocconi-StatML Summer School in Advanced Statistics and Probability
The 2026 edition of the Bocconi-StatML Summer School in Advanced Statistics and Probability will take place from July 8 to 17, 2026, at Villa del Grumello, on Lake Como.
The topic of the 2026 edition is “Causality and Graphical Models” and the main instructor is Aad van der Vaart (TU Delft).
The Bocconi-StatML Summer School in Advanced Statistics and Probability continues the tradition of the Bocconi summer school in Statistics and Probability that Università Bocconi had been organizing since the early ’90s, and were held in Torgnon, Val d’Aosta, until 2008. From 2021, the Summer School is jointly offered and organized by Bocconi University in collaboration with the University of Oxford and Imperial College, London within the Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning (StatML CDT), and is held in alternating years in the UK and in Como, Italy.
The aim of the Bocconi Summer School in Advanced Statistics and Probability is to establish a track of high-level courses on advanced and cutting-edge topics in Statistics and Probability. It offers lectures delivered by internationally leading scholars on the specific designated topic, and supervised tutorials.
The school is designed for PhD students, and possibly brilliant MSc graduates / final-year students interested in pursuing doctoral studies in Statistics, Probability, Data Science and AI, Computer Science, Applied Mathematics, Operation Research and related areas.
The school is co-sponsored by the Institute of Mathematical Statistics (IMS).
The deadline for applications is April 14, 2026.
For more information, please visit:
https://bss2026.lakecomoschool.org/
or contact: BSS.statistics@unibocconi.it
Past editions
2025: Computational Optimal Transport and Statistical Learning with Missing Values
2024: Statistical science for understanding climate and the Earth system
2023: Causality, Reinforcement Learning and Statistical Learning
2022: Random Structures and Combinatorial Statistics
2019: Random Graphs and Complex Networks: Structure and Function
2018: Graphical Models
2017: Statistical Causal Learning
