Bruno Buonaguidi is a Post-Doctoral Fellow at the InterDisciplinary Institute of Data Science at USI. His field of study is the theory of optimal stopping with applications to statistics and finance. He earned a Bachelor’s degree in Economics (2007) and a Master’s degree in Finance (2009) at the University of Pisa. He received his PhD in Statistics at Bocconi University (2014) with a dissertation on problems of sequential hypotheses testing and sequential change-point detection. He is currently the principle researcher in a project funded by the AXA Research Fund to develop early detection techniques for sudden changes occurring in certain classes of processes; the goal is to apply the results of this research to the efficient detection of frauds in credit card transactions. His work has been published in international journals of statistics, stochastic processes and sequential analysis.