Research

NLP and graph representation learning

My current postdoctoral work develops NLP methods that use graph representations and reasoning, supported by GPU-accelerated training and evaluation workflows in Python and PyTorch.

Spectral graph theory and clustering

I study discrete magnetic Laplacians, spectral bracketing, graph comparison, and clustering methods for complex graph-structured data.

• Isospectral Graphs via Spectral Bracketing
• Spectral Preorder and Perturbations of Discrete Weighted Graphs

Graph signal processing and biomedical data

I develop entropy and complexity measures for signals on graphs, with applications to EEG, fMRI, DTI networks, two-phase flow, and other noisy multivariate data.

• Permutation Entropy for Graph Signals
• Dispersion Entropy for Graph Signals
• Graph-based Permutation Patterns for fMRI Signals

Public-sector analytics

At INEE in Mexico, I worked with national assessment and census data using multilevel modelling, graph-based clustering, GIS equity maps, reproducible R workflows, and Shiny dashboards for policy-facing analysis.