Summary
Ximena Fernández is a Faculty Lecturer and applied mathematician with eight years of research and industry experience translating advanced algebraic and combinatorial topology into computational tools for real-world problems. With a PhD focused on combinatorial topology and a string of postdoctoral positions at Durham, Swansea and Oxford, she now specializes in Computational Topology, Topological Data Analysis and Persistent Homology, often combining these with machine learning to extract geometric insight from complex datasets. Her work spans pure theory and practical deployment—she has built combinatorial and visualization methods and deployed ML models in industry settings such as property valuation and energy-demand forecasting. Based in the UK, she brings deep expertise in finite topological spaces and homotopy theory to interdisciplinary teams, and is notable for bridging rigorous proofs with reproducible computational workflows.
9 years of coding experience
4 years of employment as a software developer
Research stay, Persistent Homology, Research stay, Persistent Homology at Universidad de Málaga
Postdoc, Topological Data Analysis, Postdoc, Topological Data Analysis at University of Oxford
Postdoc, Topological Data Analysis, Postdoc, Topological Data Analysis at Swansea University
Postdoc, Topological Data Analysis, Postdoc, Topological Data Analysis at Durham University
Licentiate degree, Mathematics, Licentiate degree, Mathematics at University of Buenos Aires
Research stay, Research stay at Nagoya University
English