Bocconi Postdoc Honored with 2024 Laplace Award for Advancements in Bayesian Statistics

Francesco Pozza, postdoctoral researcher at the Bocconi Institute for Data Science and Analytics, has been awarded the Laplace Award by the American Statistical Association Section on Bayesian Statistical Science (SBSS) in recognition of his outstanding contributions to the field of Bayesian statistics. His paper, “Skewed Bernstein-von Mises theorem and skew-modal approximations”, co-authored with Daniele Durante and Botond Szabo (both of Bocconi’s Department of Decision Sciences), introduces novel methodologies that address shape constraints in regression models using a Bayesian framework. Pozza's research is celebrated for its balance between applied, computational, and theoretical advancements, which significantly improve the precision and efficiency of Bayesian inference methods.

The paper addresses important challenges in shape-restricted regression by developing generalized Bayesian methods that ensure more robust and accurate statistical models, particularly useful in fields like economics, medicine, and environmental sciences where such constraints are common. His innovative approaches have broad applications, promising to push forward both the computational strategies used in Bayesian inference and the broader theoretical understanding of constrained models.

The Laplace Award, named after the renowned French mathematician Pierre-Simon Laplace, is presented annually to an individual or group whose contributions significantly advance Bayesian statistical science.