Andrea Raffo

Andrea Raffo serves as a Researcher in the Department of Molecular Biosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Computational 3D Genomics research group led by Paulsen. His work bridges computational mathematics and biological applications through advanced geometric analysis techniques.

His educational background includes a PhD in Mathematics from the University of Oslo, preceded by Bachelor's and Master's degrees in Mathematics from the University of Genoa (Italy). This strong mathematical foundation enables his specialized research at the intersection of computational geometry and life sciences.

• Research Focus: Development of computational methods for geometric pattern recognition in biological systems
• Core Methodologies: 3D point cloud analysis, shape characterization, and algorithmic pattern detection
• Application Domains: Chromosome conformation capture data analysis, protein channel dynamics, and super-resolution microscopy image processing

His publication record demonstrates consistent contributions to computational geometry with direct applications in biosciences. Recent work shows increasing specialization in translating geometric pattern recognition techniques to solve concrete problems in genomics and structural biology, particularly through participation in international benchmarking initiatives like SHREC.

While no formal awards are documented in current records, his research has been consistently published in high-impact venues including Computer-Aided Design, Computer Aided Geometric Design, and Frontiers in Molecular Biosciences. His collaborative approach is evident through extensive co-authorship networks across European and Asian research institutions.

Raffo actively contributes to the Computational 3D Genomics group, focusing on developing analytical tools for chromosome architecture analysis and protein structure characterization. His current projects involve advanced segmentation techniques for HP1α condensate structures in super-resolution microscopy, representing the cutting edge of computational bioscience methodology development.