Simone Tedeschi

About

Simone Tedeschi is a researcher at the forefront of human–robot interaction and affective computing, with a specialized focus on developing machine learning systems that can assess and respond to human attention. His most-cited work tackles a critical bottleneck in the field: data scarcity. By constructing a custom dataset of approximately 120,000 photographs and employing a GAN-based data augmentation technique, Tedeschi’s 2022 study (3 citations) demonstrated a novel, scalable approach to training attention-assessment models. This contribution is foundational for creating more intuitive and responsive robotic systems that can reliably gauge user engagement. Beyond this pivotal study, Tedeschi’s research bridges computer vision, generative adversarial networks, and human-centered design, aiming to make human–robot interactions safer and more natural. His work on robust data augmentation strategies offers a practical solution for researchers facing limited real-world data, and his custom dataset serves as a valuable resource for the community. Tedeschi’s achievements reflect a commitment to advancing the reliability and applicability of attention-aware technologies in real-world settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

   3 citations · 2022

Key Collaborators

Contact & Links