Rate My Professor Fabio Pierazzi

Prior to his current role, which began in September 2019, Pierazzi held postdoctoral positions at the Systems Security Research Lab (joint RHUL and KCL, 2017–2019), University of Modena (2017), and was a visiting research scholar at the University of Maryland College Park (2016). He supervises PhD students on topics including federated learning security, explainability and adversarial robustness, problem-space adversarial robustness, and concept drift anticipation. His research focuses on machine learning for systems security, with emphasis on malware analysis, network intrusion detection, adversarial machine learning, and countermeasures against adaptive attackers in non-stationary contexts. Pierazzi has authored papers in top venues such as USENIX Security and IEEE S&P, including 'Dos and Don’ts of Machine Learning in Computer Security' (USENIX Security 2022, Distinguished Paper Award), 'TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time' (USENIX Security 2019), 'Intriguing Properties of Adversarial ML Attacks in the Problem Space' (IEEE S&P 2020), and 'Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift' (IEEE S&P 2022). He received the EPSRC New Investigator Award in 2023 for robust explanations in malware detection and the Faculty Student Support Award in 2024. Pierazzi serves on program committees for IEEE S&P, USENIX Security, ACM CCS, AAAI, and others, and reviews for IEEE TIFS and ACM TOPS.