Fabrizio Ruggeri
CNR-IMATI
Title: Adversarial Classification
Abstract: In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates in search of certain goals. Such problems pertain to the field of adversarial machine learning and have been mainly dealt with, perhaps implicitly, through game-theoretic ideas with strong underlying common knowledge assumptions. These are not realistic in numerous application domains in relation to security. We present an alternative statistical framework that accounts for the lack of knowledge about the attacker’s behavior using adversarial risk analysis concepts.
Bio: Dr. Ruggeri is an Italian statistician and has been a Senior Fellow at CNR-IMATI (Institute of Applied Mathematics and Information Technology at the Italian National Research Council) since his retirement from the Research Director position there in May 2023. He holds a doctoral degree in statistics from Duke University. His work focusses on Bayesian methods, specifically robustness and stochastic process inference. He has done innovative work on the sensitivity of Bayesian methods and incompletely specified priors. He has also worked on Bayesian wavelet methods, and on a vast variety of applications to industrial problems. His publications include well over 150 refereed papers and book chapters, as well as six books. He is an elected member of the International Statistical Institute (ISI) and a fellow of the American Statistical Association (ASA), the International Society for Bayesian Analysis (ISBA) and the Institute of Mathematical Statistics (IMS).
Date: Friday, March 6, 2026, 12:00 PM, AUST 163
WebEx link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=md16536e6483004e964b823205601a4d8
Coffee will be available at 11:30 AM in the Noether Lounge (AUST 326)
For more information, contact: Yuwen Gu at yuwen.gu@uconn.edu