Summary
Raphaël Romero is a postdoctoral researcher and engineer specializing in applied mathematics and computer science, with a decade of experience bridging statistical modeling and dynamic graph analysis. Based at AIDA (UGent), he develops multiresolution and temporal graph methods—work that has led to NeurIPS and ECML-PKDD workshop contributions on neural ODE-based graph encoders and network thresholding. His PhD focused on modeling weighted and temporal graphs, and he has a track record of cross-disciplinary collaborations including a research visit to Imperial College London. Prior industry experience deploying fraud-detection systems and building ML prototypes gives him practical product-minded instincts alongside rigorous theory. Fluent in both research and applied settings, he combines strong mathematical training from Télécom Paris and ENS Paris-Saclay with hands-on implementation skills across languages and data systems. An understated strength is his ability to translate complex temporal-graph theory into tools and evaluations that address real-world spatiotemporal problems, from sports analytics to ecology.
10 years of coding experience
1 year of employment as a software developer
Master 2 (Msc), MVA (Applied Mathematics, Machine Learning and Computer Vision), Master 2 (Msc), MVA (Applied Mathematics, Machine Learning and Computer Vision) at École normale supérieure Paris-Saclay
Master of Engineering - MEng, Applied Mathematics, Computer Science, Economics, Master of Engineering - MEng, Applied Mathematics, Computer Science, Economics at Telecom ParisTech
Mathematics, Mathematics at Lycée Charlemagne, Classes Préparatoires aux Grandes Ecoles d'Ingénieur