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Investigating how these sensors perceive environments, particularly in adverse weather conditions like rain.
In the world of autonomous driving and smart sensing, "seeing" isn't enough—sensors must understand. While LiDAR and cameras have made massive leaps, they often struggle when nature gets messy. This is where the intersection of and Machine Learning becomes the most exciting frontier in engineering. The Challenge of "Noisy" Environments
He then secured the Singapore International Graduate Award (SINGA) to pursue a Ph.D. at Nanyang Technological University (NTU), graduating in 2026. Concurrently, he conducted collaborative research alongside elite scientists at the Institute for Infocomm Research ( I2Rcap I squared cap R richard capraru
Capraru’s research spans several cutting-edge domains in computer science and electrical engineering: Autonomous Vehicle Security
is an international researcher specializing in the intersection of robust autonomous systems, adversarial perception, and cybersecurity . Currently affiliated with the International Research Center for Neurointelligence (IRCN) at the University of Tokyo, his academic work addresses the core vulnerabilities of 3D vision and sensing networks, particularly Light Detection and Ranging (LiDAR) and Radar technologies used in self-driving vehicles. This is where the intersection of and Machine
Addressing the computational constraints of deploying attacks in real time, Dr. Capraru developed (2026). The framework optimizes the physics of sensor spoofing, mathematically reducing the volume of data an attacker must inject into a spinning LiDAR stream. By executing data minimization principles, GhostLite proves that real-time, low-latency spoofing attacks are achievable against consumer-grade AV hardware without requiring massive, external computing rigs. 3. Radar Signal Processing and the Dop-NET Challenge
: Richard is a PhD candidate in the School of Electrical and Electronic Engineering at Nanyang Technological University (NTU) and the Institute for Infocomm Research at the Agency for Science, Technology and Research (A*STAR). Technology and Research (A*STAR).
Beyond environmental interference, autonomous sensors face targeted adversarial disruptions. Dr. Capraru analyzes where bad actors strategically manipulate environmental reflections or transmit spoofed signals to trick machine learning models. His doctoral research addresses how easily standard 3D object detection pipelines can be blinded or deceived, laying the groundwork for cryptographic and algorithmic defenses within Advanced Driver-Assistance Systems (ADAS).
(UCL), where he earned his Bachelor of Engineering. During his time at UCL, he was recognized as a Laidlaw Scholar
Presented at the Advanced Concepts for Intelligent Vision Systems (ACIVS) conference, detailing point cloud reconstruction during adverse weather.
R) under Singapore's Agency for Science, Technology and Research (A*STAR). He also completed a specialized research attachment at Imperial College London alongside experts in computer systems security. Core Research Areas
