Best fit roles
Research EngineerApplied ScientistML Engineer (structured data / graph ML / scientific ML)
CV
A quick recruiter view.
A compact summary of my current role, skills, selected experience, and selected publications.
John Stewart Fabila-Carrasco
Research Engineer in NLP, Graph ML, and Structured Data
I work at the intersection of NLP, graph ML, and structured-data analysis, with hands-on experience in research engineering, scientific ML, and stakeholder-facing analytics.
Skills
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
Quick profile
- Current postdoctoral work in NLP and graph representation learning
- Previous work on scalable graph clustering for large graph-structured datasets
- Scientific ML experience across EEG, fMRI, DTI, and difficult healthcare data
- Public-sector analytics with reproducible R workflows and Shiny dashboards
Selected experience
Experience behind the work.
Selected roles across NLP, graph clustering, scientific ML, and public-sector analytics.
Postdoctoral Researcher in NLP and Graph Representation Learning
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.
Sep 2025 - Apr 2027
AI/MLNLPGraph MLPyTorch
Postdoctoral Researcher in Scalable Spectral Clustering
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.
Feb 2024 - Apr 2025
Graph MLScalable learningClusteringPython
Postdoctoral Researcher in Graph Signal Processing for Biomedical Data
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.
Nov 2020 - Jan 2024
HealthcareGraph Signal ProcessingEEGNeuroimaging
Industry and Government Data Science Researcher
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.
Feb 2014 - Sep 2015
Applied analyticsDecision supportDashboardsPublic sector
Selected publications
Publications I would highlight first.
A few papers that anchor the main research themes.
International Conference on Machine Learning (ICML) • 2025
Signed Laplacians for Constrained Graph Clustering
A graph clustering method that respects constraints while remaining mathematically principled and scalable enough to matter for real structured datasets.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2024
Graph-based Permutation Patterns for the Analysis of Task-related fMRI Signals on DTI Networks in Mild Cognitive Impairment
A way to study how brain activity evolves across anatomical networks rather than treating signals as isolated time series.
Chaos, Solitons and Fractals • 2023
Dispersion Entropy for Graph Signals
An entropy measure designed for graph-shaped data, making it easier to quantify complexity in signals that depend on network structure.
European Signal Processing Conference (EUSIPCO) • 2023
Graph-based Multivariate Multiscale Permutation Entropy: Study of Robustness to Noise and Application to Two-Phase Flow Data
Shows how graph-based complexity measures hold up when data are noisy and messy, not just mathematically clean.
Contact
For research collaboration, technical conversations, or applied AI work involving language, graphs, or difficult structured data, email is the best way to reach me.