Sveva Pepe

About

Sveva Pepe is a researcher at the forefront of human–robot interaction and computer vision, with a specialized focus on attention assessment and data augmentation. Her most-cited work tackles a critical bottleneck in robotics: the scarcity of training data for attention-detection systems. By developing a machine learning approach that leverages Generative Adversarial Networks (GANs) for data augmentation, she constructed a custom dataset of approximately 120,000 photographs, enabling robust training of attention-assessment models. This contribution directly enhances the ability of robots to interpret human focus, a key step toward more intuitive and responsive human–robot collaboration. With 3 citations, her 2022 study has already sparked interest in the field, demonstrating the practical impact of her work. Pepe’s research not only advances the technical capabilities of interactive systems but also addresses a fundamental challenge in deploying AI in real-world scenarios. Her innovative use of GANs to overcome data limitations marks her as a promising voice in the intersection of machine learning and robotics.

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