Gaetano Saurio

Gaetano Saurio

PhD candidate in Data Science Sapienza, University of Rome

Applied AI & Robotics

In this section, I showcase projects that bridge the gap between creative prototyping, industrial applications, and social impact through Robotics and AI.

Professional & Industrial Experience

Big Data Advanced Analytics Applications Led the development of end-to-end Advanced Analytics solutions for a major IT enterprise.

  • Architecture: Designed distributed pipelines integrating microservices, IoT, and Big Data technologies to process industrial machinery telemetry.
  • Impact: Delivered a scalable monitoring system that optimized maintenance schedules and increased sales for industrial clients.

Corporate Training & Tech Knowledge Transfer

  • Leonardo S.p.a.: Delivered specialized technical training on Lean Production, Industry 4.0, and Soft Skills.
  • Innovation Ecosystems (2025): Technical exchanges at Kilometro Rosso Innovation District (FAE Technology HUB) and the Copenhagen Institute of Interaction Design (CIID), focusing on AI-human interaction and sustainable IoT.

Research Prototyping & Maker Activities

Poommarobot: Social Robotics & Android Integration Development of an innovative platform for a remote-controlled social robot.

  • Tech Stack: Integrated autonomous behaviors by leveraging Android smartphone sensors and processing power as the robot’s “brain”.
  • Recognition: Awarded for Best Innovation; presented as a keynote speaker at various international innovation talks.
  • Official Website

The Cybergarden: AI-Driven Agricultural Robotics An ongoing project to develop cost-effective, smartphone-based robots for sustainable farming.

  • Vision: Utilizing Deep Learning for species recognition, disease monitoring, and autonomous harvesting.
  • Goal: Create accessible technology for economically disadvantaged areas, fostering a unique interconnection between humans and the plant kingdom.
  • The Cybergarden

Sonic Health Insight: Deep Learning for Bio-Acoustics Researching the use of Deep Learning to extract latent health indicators from human speech recordings. This project explores the intersection of signal processing and medical AI to decode subtle vocal features related to well-being and clinical diagnostics.