Academic website
Graph-based methods for data, networks, and signals.
My research connects mathematical foundations with applied analytics, from magnetic Laplacians and graph signals to NLP, biomedical data, and public-sector decision support.
Education
Universidad Carlos III de Madrid, 2020
Excellent Grade 10/10, Cum Laude, International Distinction, and Outstanding Thesis Award.
Universidad Carlos III de Madrid, 2016
Thesis on spectral gaps of magnetic Laplacians on graphs.
National Autonomous University of Mexico, 2012
Built the theoretical foundation that later expanded into graph and data-driven methods.
Autonomous Mexico State University, 2010
Early training in rigorous mathematical modelling and analysis.
Research experience
Cardiff University, Cardiff, Wales, UK
Developing NLP methods that combine language modelling with graph reasoning, supported by GPU-accelerated training and evaluation workflows in Python and PyTorch.
University of Edinburgh, School of Informatics, Edinburgh, Scotland, UK
Designed clustering algorithms for large graph-structured datasets, with a focus on computational efficiency and scalable experimentation for high-dimensional network analysis.
University of Edinburgh, School of Engineering, Edinburgh, Scotland, UK
Developed nonlinear graph-signal methods for EEG and neuroimaging time series, leading the computational analysis work for a Leverhulme-funded project on difficult healthcare data.
National Institute of Educational Evaluation (INEE), Mexico City, Mexico
Led end-to-end statistical modelling for nationwide assessment and census data, building reproducible R pipelines and Shiny dashboards for policy and resource-allocation decisions.
Institute of Mathematical Sciences (ICMAT), Madrid, Spain
Applied magnetic-Laplacian and spectral-bracketing techniques to extract structure from weighted graphs and sharpen graph-based clustering and classification routines.
Research themes
Magnetic Laplacians, spectral preorders, eigenvalue bracketing, and isospectral graph constructions.
Entropy and permutation-based methods for signals defined on graphs, including biomedical and industrial time-series.
Graph representation learning, structured reasoning, scalable experiments, and GPU-accelerated model development.
Reproducible workflows, dashboards, geospatial analysis, and data products for public-sector decision support.
Publications
Teaching and supervision
Contact and profiles
