Francesca Nonis

Francesca Nonis is an Assistant Professor and Fixed-term researcher at Politecnico di Torino's Department of Management and Production Engineering (DIGEP). She is an invited member of both the College of Architecture and Design and the College of Mechanical, Aerospace, and Automotive Engineering. As part of the 3D LAB research group, she contributes to cutting-edge research in human-computer interaction applications and 3D data visualization.

Dr. Nonis specializes in Facial Expression Recognition (FER) using Deep Learning techniques, with a focus on 3D data applications for human-computer interaction. Her research spans multiple domains including virtual reality, affective computing, and cultural heritage preservation. She has developed methodologies for real-time facial expression recognition and has contributed to building spontaneous 3D facial expression databases that have advanced the field of emotion recognition.

Her recent publications reveal a strong trend toward interdisciplinary research that bridges computer science, psychology, and cultural applications. She has made significant contributions to metaverse platforms for cultural heritage preservation, user engagement studies using EEG in museum settings, and the development of affective virtual reality environments. Her work consistently applies advanced computer vision and deep learning techniques to solve real-world problems in human-computer interaction.

Dr. Nonis has co-authored a national patent titled "Methodology for real-time facial expression recognition" with Federica Marcolin, Luca Ulrich, and Enrico Vezzetti. Her teaching portfolio includes Industrial Technical Drawing and Modeling Design courses across multiple engineering disciplines including Aerospace Engineering, Mechanical Engineering, Chemical and Food Engineering, and Design and Communication programs.

As a core member of the 3D LAB research group, Dr. Nonis collaborates on projects involving facial morphology studies, innovative surgical guides, augmented reality applications, and e-learning solutions. The lab's work leverages 3D sensors, augmented and virtual reality, and machine learning techniques to develop applications for medical, security, and marketing domains.