Francesco Mancuso
Researcher at RaSS National Inter-University Consortium for Telecommunications (CNIT)
Curriculum Vitae
Profile
I'm a researcher at CNIT's RaSS National Lab in Pisa, where I also completed my PhD. My doctoral work focused on 3D radar imaging and on applying AI to recognize non-cooperative targets. I continue to follow that line of research, though my focus has since moved toward electronic warfare and cognitive systems.
Experience
Electronic Warfare, C-ESM, Array Processing.
Radar Interferometry, Radar Polarimetry, ISAR Imaging, Multi-channel Radar, Synchronization.
Standard Tutorial, Marking.
Statistical Signal Processing, Anomalies Detection, Structure Health Monitoring, Wireless Sensors Networks, Electromagnetic Compatibility.
Education
Information Engineering Sector, Section A
Thesis title: Novel 3D Interferometric Inverse Synthetic Aperture Radar Imaging Techniques for Non-Cooperative Target Recognition
Final mark: Excellent
Thesis title: Development and validation of Polarimetric Three-Dimensional ISAR imaging techniques.
Final mark: 110/110 cum laude
Thesis title: IoT-based botnet and DNS DDoS Attacks: Dyn's case analysis.
Final mark: 108/110
Industrial Engineering: Electronic and Telecommunications
Final mark: 100/100
Awards & Achievements
IEEE Radar Conference 2025 — issued by IEEE Aerospace and Electronic Systems Society (AESS).
Joint SET–SCI Spring PBM 2025 — issued by NATO STO Sensors and Electronics Technology (SET) Panel.
For his significant scientific contribution in the Exploratory Team SET-ET-128, which evolved into SET-335, "RFT-OT Data and Sharing Hub (DASH)." As co-chair, he was instrumental in developing a NATO-internal database for high-quality sensor datasets, which supported NATO's Digital Transformation by enabling the integration of emerging technologies like AI. His efforts exemplify the collaborative spirit and forward-thinking essential to NATO's mission.
Eurosatory 2024 — issued by the European Defence Agency.
SET-318 Specialists' Meeting on "Artificial Intelligence (AI) / Machine Learning (ML) for Cognitive Radar (CR)" — issued by NATO Sensors and Electronics Technology Panel.
Organizing Committee Member. Presentation: "Evaluating Killer Drone Defense: NATO SPS Project Anti-Drones Field Trials".
IEEE LCN 2023 — issued by IEEE Computer Society TCCC.