Ms. Martina Formichini | Artificial Intelligence | Best Researcher Award 

Ms. Martina Formichini | Artificial Intelligence | Sant’Anna School of Advanced Studies | Italy

Ms Martina Formichini is an Italian researcher whose interdisciplinary academic and professional background positions her strongly within the domains of physics, artificial intelligence, remote sensing, and large-scale data analytics. Ms Martina Formichini completed her Bachelor’s Degree in Physics at Sapienza University of Rome, followed by a Master’s Degree in Physics of Biosystems at the same institution, where she developed foundational expertise in top-down visual perception modelling using fMRI and in the application of physical-statistical methods to complex economic and technological networks. She further strengthened her skill set through a Master in Big Data Analytics & Social Mining at the University of Pisa, gaining advanced training in data science, neural networks, scalable architectures, and machine learning for satellite imagery. Professionally, Ms Martina Formichini worked in research collaboration at Sapienza University investigating motif significance in economic-technological networks, later serving as a Programmer at Eustema S.p.A., a Senior Analyst and Solution Developer at Avanade, and an intern at Almaviva S.p.A., where she contributed to deep learning projects in computer vision and environmental monitoring using aerial and satellitar imagery. Her current role as a Ph.D. researcher at Scuola Superiore Sant’Anna focuses on artificial intelligence systems for terrain, vegetation, and soil classification, using segmentation techniques and deep learning frameworks. Her research interests include AI-based remote sensing, environmental monitoring, image segmentation, complex networks, NLP, statistical modelling, and high-performance data processing. Ms Martina Formichini possesses strong skills in machine learning, computer vision, Python ecosystems, SQL, scalable analytics, cloud-based cognitive services, data engineering workflows, and end-to-end predictive modelling. Her collaborative research mindset, leadership in group projects, and experience across academic and industrial settings demonstrate strong potential for impactful multidisciplinary contributions.

 Selected Publications

A Comparative Analysis of Deep Learning-Based Segmentation Techniques for Terrain Classification in Aerial Imagery

Deep Learning-Based Segmentation for Terrain Classification in Aerial Imagery