Tommaso Zoppi is currently a Research Associate at UNIFI, RCL Group.

He received his Bachelor and Master degree in Computer Science from the University of Firenze, Italy, in October 2012 and July 2014, respectively. From November 2014 to November 2017 he was a PhD student at the Matematics, Computer Science, Statistics course (curriculum: Computer Science) at the same university, under the supervision of Prof. Andrea Bondavalli. 

After almost two years of post-doc at the same university, Tommaso is now a Research Associate (RTD-A) at the Department of Mathematics and Informatics.

Despite his main research efforts are directed to anomaly detection and its applications to several domains related to critical systems, his research topics have a wide span:

• anomaly-based intrusion detection
• safety-critical architectures and V&V processes for the railway domain
• applying machine learning in safety-critical systems
• cyber-security analysis of smart grids
• crisis management systems  

Office:

Dipartimento di Matematica e Informatica (DiMaI), Room T19 (Stanza Dottorandi)

Viale Morgagni, 65 - 50134 - Firenze Italy

CV Almalaurea

• Journal M. Gharib, T. Zoppi and A. Bondavalli. "On the Properness of Incorporating Binary Classification Machine Learning Algorithms into Safety-Critical Systems", IEEE Transactions on Emerging Topics in Computing. 2022. [More] 
• Conference T. Zoppi, A. Ceccarelli and A. Bondavalli. "Towards a General Model for Intrusion Detection: An Exploratory Study". Machine Learning for CyberSecurity (MLCS 2022). 2022. [More] 
• Conference A. Muhammad, T. Zoppi, M. Gharib and A. Bondavalli. "Quantitative comparison of supervised algorithms and feature sets for traffic sign recognition". SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing. ACM ed. 2021. pp. 174-177. [More] 
• Conference T. Zoppi, A. Ceccarelli and A. Bondavalli. "Detecting Intrusions by Voting Diverse Machine Learners: Is It Really Worth?". Pacific Rim Dependable COmputing (PRDC2021). 2021. [More] 
• Conference M. Gharib, T. Zoppi and A. Bondavalli. "Understanding the properness of incorporating machine learning algorithms in safety-critical systems". SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing. ACM ed. 2021. pp. 232-234. [More]