Dear all, We are glad to inform you that ABS26, the next Applied Bayesian Statistics school, running since 2004, will be held in the city of Como (Italy), along the Lake Como shoreline at the beautiful Villa del Grumello, on July 6-10, 2026. The school is organised by CNR IMATI (Institute of Applied Mathematics and Information Technologies at the Italian National Research Council in Milano), in cooperation with Fondazione Alessandro Volta. The topic will be Interpretable Bayesian Learning for Physical and Engineering Sciences. The lecturer will be Prof. Simon Mak (Department of Statistical Science, Duke University), a leading expert in integrating domain knowledge (e.g., scientific theories, mechanistic models, guiding principles) as prior information for cost-efficient statistical inference, prediction, and decision-making, with the support of Yen-Chun Liu (Department of Statistical Science, Duke University), a brilliant PhD student of Prof. Mak, specializing in Bayesian optimization, Gaussian processes, and reinforcement learning. If interested, you can register on the school website: https://abs26.imati.cnr.it/ Early registration fees are available until April 30, 2026. As in the past, there will be a combination of theoretical and practical sessions, along with presentations by participants on their work (past, present, and future) related to the school's topic. OUTLINE: This course investigates the rising topic of interpretable statistical learning, motivated by timely needs from modern scientific and engineering applications. For such applications, state-of-the-art machine learning methods often yield analyses that are uninterpretable to scientists, which greatly obfuscates scientific findings and decision-making. Interpretable Bayesian learning provides an elegant and effective solution by embedding scientific principles (e.g., boundary conditions, mechanistic equations) within its prior specification. In this course, we will cover a broad spectrum of interpretable Bayesian learning methods (with theory and algorithms), providing a cohesive roadmap for this recent and rapidly-evolving area of study. This will be complemented by practical case studies from ongoing projects in particle physics, mechanical engineering, and bioengineering. Emphasis will be made on novel directions for research in this promising area. Topics will cover - Fundamentals of Gaussian process (GP) surrogate modeling: prediction, parameter inference, experimental design - Decision-making with GP surrogates: Bayesian optimization, active learning, Bayesian inverse problems - Markov Chain Monte Carlo (MCMC) methods - Boundary-informed GP modeling - Shape-constrained (e.g., monotone, isotonic, convex) GP modeling - Mechanistic GP modeling - Physics-integrated neural networks, its Bayesian variants and recent developments - Dynamical system recovery: SINDy, Bayesian SINDy, and recent developments We hope you will be interested in the school and we would like to meet you in Como. We invite you also to share the information with people potentially interested. Best regards Elisa Varini and Fabrizio Ruggeri Executive Director and Director of ABS26