Bio

I am a cybersecurity and IoT researcher with a passion for developing secure, reliable, and intelligent systems that address real-world challenges. My work focuses on enhancing the security of low-power wide-area networks (particularly LoRaWAN) through lightweight key management schemes, while also exploring advanced machine learning techniques for human health monitoring and sensor data analysis.

My research interests span cybersecurity in IoT, cryptographic protocols, secure key distribution, and deep learning applications for time-series prediction. I have worked extensively on developing novel methods to mitigate cyber attacks in LoRaWAN networks, including secure key exchange mechanisms that balance performance and resource constraints. Additionally, I have developed deep learning models to predict human postural stability using IMU data, supporting accessible health-monitoring solutions.

Beyond IoT security, my background includes signal processing, underwater wireless sensor networks (UWSNs), and applied cryptography. I am dedicated to interdisciplinary collaboration, combining AI, security, and communication technologies to deliver robust, scalable, and practical solutions.

I am a Postdoctoral Researcher at DITEN, UniGe, where I focus on human balance evaluation using deep learning with wearable IMU data. Previously, I worked with CNIT Genova Unige on 5G core networks and secure IoT systems. My research brings together machine learning, cybersecurity, and sensor analysis to deliver practical solutions for connected health and communications.