Simone Sorrenti

Background

About Simone Sorrenti

With a solid foundation in Computer Science and a specialization in Artificial Intelligence, I blend academic excellence with hands-on experience to build innovative AI solutions. After earning a Bachelor's in Computer Engineering from the University of Bari, I completed a Master's in Computer Science at the Polytechnic University of Milan, where I focused on deep learning and developed a thesis on illegal landfill detection using weakly supervised learning. Currently, I work as an R&D AI Engineer at TXT E-TECH, contributing to cutting-edge aerospace and defense projects. My role involves designing AI systems for cockpit component validation through computer vision and audio recognition. I’ve developed ROS2-based frameworks to integrate robotic, visual, and audio modules; applied and optimized object detection models like DETR and Florence2; and built an audio verification system using MFCCs, DTW, and cosine similarity. I also lead solar panel inspection tasks, using autoencoders and classifiers for anomaly detection with high recall and precision. Previously, as a student researcher for the European project Perivallon, I developed CNN and Transformer-based models for detecting waste sites from satellite imagery, implementing full training pipelines with data augmentation, loss optimization, and hyperparameter tuning. I also adopted weakly supervised techniques to improve segmentation without pixel-level annotations. Earlier in my career, I worked as a full-stack developer in the banking and insurance sectors, creating backend services and dynamic front-end interfaces to automate business processes. Passionate about applied AI, I actively engage with the tech community through Kaggle competitions, dataset creation, and open-source contributions. My tech stack includes Python, PyTorch, TensorFlow, ROS2, OpenCV, Hugging Face, as well as proficiency in C++, Java, and SQL. Outside of work, I enjoy hiking, traveling, and cooking—finding creativity in the exploration of cultures and technologies.