Gabriele Inzerillo received his B.Sc. in Computer Engineering from the University of Palermo and then completed an M.Sc. in Computer Engineering at Politecnico di Torino, where he concentrated on software engineering and fundamentals of artificial intelligence, machine learning and deep learning. For his master’s thesis and internship he developed a multi-image super-resolution model for cross-sensor fusion, building two novel Proba-V and Sentinel-2 datasets.
Following graduation, Gabriele worked in industry as a Machine Learning Engineer and then as an Applied Scientist specializing in deep learning and computer vision for remote sensing. Between 2023 and early 2024 he contributed to build an efficient, self-supervised feature extractors and task-specific multi-head architectures, and implemented quantization and deployment pipelines for low-power demonstrators and space-qualified hardware (Unibap iX5).
Since March 2024 Gabriele has been pursuing a PhD in Deep Learning at Politecnico di Torino as part of a joint project with the European Space Agency. His research focuses on designing efficient deep learning architectures for low-power, on-board satellite systems — with particular emphasis on low-latency vision tasks and practical solutions to hardware constraints. Although his main body of work lies in lightweight computer vision models and remote sensing, he is broadly passionate about deep learning and open to tackling problems across different data modalities.
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