Curriculum vitae

My experience spans NLP, graph machine learning, biomedical signals and public-sector data analysis.

Research experience

Sep 2025 - Apr 2027: Postdoctoral Researcher in NLP and Graph Representation Learning, Cardiff University.
I develop NLP methods using graph representations and reasoning, with reproducible GPU-accelerated training and evaluation workflows in Python, PyTorch and CUDA.
Feb 2024 - Apr 2025: Postdoctoral Researcher in Scalable Spectral Clustering, University of Edinburgh, School of Informatics.
I designed clustering methods for large graph-structured datasets, focusing on computational efficiency and reproducible experiments for high-dimensional network analysis.
Nov 2020 - Jan 2024: Postdoctoral Researcher in Graph Signal Processing for Biomedical Data, University of Edinburgh, School of Engineering.
I developed nonlinear graph-signal methods for EEG and neuroimaging time series within a Leverhulme-funded project on healthcare data.
Jun 2020 - Oct 2020: Researcher in Spectral Methods on Graphs, Institute of Mathematical Sciences (ICMAT).
I studied magnetic Laplacians and spectral bracketing, developing mathematical methods for analysing and comparing weighted graphs.
Feb 2014 - Sep 2015: Industry and Government Data Science Researcher, National Institute of Educational Evaluation (INEE).
I analysed national assessment and census data, combining statistical modelling, geographic and socioeconomic data, and reproducible R/Shiny dashboards for policy teams.

ML and experimentation: Python, PyTorch, CUDA, Hugging Face, Jupyter.
Data and analysis: NumPy, Pandas, R, MATLAB, Statistical modelling.
Applied domains: NLP, Graph ML, Graph signal processing, Decision-support dashboards.

2020: PhD in Mathematical Engineering, Universidad Carlos III de Madrid. Excellent Grade 10/10, Cum Laude, International Distinction, and Outstanding Thesis Award.
2016: MSc in Mathematical Engineering, Universidad Carlos III de Madrid. Thesis on spectral gaps of magnetic Laplacians on graphs.
2012: MSc in Mathematical Sciences, National Autonomous University of Mexico. Built the theoretical foundation that later expanded into graph and data-driven methods.
2010: BSc in Mathematics, Autonomous Mexico State University. Early training in rigorous mathematical modelling and analysis.

15 peer-reviewed papers. Selected venues include ICML, ICASSP, EUSIPCO, IEEE TSIPN, and Linear Algebra and its Applications.

Signed Laplacians for Constrained Graph Clustering. International Conference on Machine Learning (ICML), 2025.
Isospectral Graphs via Spectral Bracketing. Linear Algebra and its Applications, 2024.
Graph-based Permutation Patterns for the Analysis of Task-related fMRI Signals on DTI Networks in Mild Cognitive Impairment. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
Dispersion Entropy for Graph Signals. Chaos, Solitons and Fractals, 2023.
Permutation Entropy for Graph Signals. IEEE Transactions on Signal and Information Processing over Networks, 2022.

Teaching: 12+ years teaching, 250+ contact hours, and three MSc co-supervisions.
2022: Alan Turing Institute Post-Doctoral Enrichment Award, The Alan Turing Institute.
2021: Professional Development Grant, Society of Spanish Researchers in the UK (SRUK).
2021: Outstanding Thesis Award, Universidad Carlos III de Madrid.
2020: Teaching Excellence Award, Universidad Carlos III de Madrid.
2018: Researcher Mobility Grant, Universidad Carlos III de Madrid.
2017: Pre-doctoral Research Training Scholarship (PIPF), Department of Mathematics, Universidad Carlos III de Madrid.
2020: Doctoral Lecturer Accreditation, ANECA.