Applied AI & research software for engineering and environmental systems
Digital twins · anomaly detection · sensor-data QA/QC · scientific Python · environmental and industrial systems
I'm a Research Fellow and Data Scientist at the University of Edinburgh, working at the interface of machine learning, sensor data, scientific modelling, and engineering and environmental systems.
My work turns complex physical-system data into reproducible tools for monitoring, validation, anomaly detection, model interpretation, and decision support.
I'm especially interested in applied AI for sensor-rich systems—including structural testing, hydrology and hydraulics, environmental monitoring, and scientific machine learning—where reliable data pipelines, validation, and domain knowledge are as important as the model itself.
The best starting points are:
- Scientific ML / geomorphology:
meander-morphology-classifier - Digital twins / industrial AI:
synthetic-hydraulic-digital-twin-demo - Applied ML / anomaly detection:
audio-anomaly-detection-structural-testing - Environmental monitoring / sensor QA/QC:
urban-drainage-sensor-data-toolkit - Scientific modelling / hydrology:
LDSFL_Meander - Engineering data QA/QC:
tdms-sync-checker
Together, these projects show how I approach applied AI beyond model fitting: data quality, reproducibility, validation boundaries, scientific interpretation, and honest documentation of assumptions and limitations.
- Urban drainage telemetry QA/QC: Launch demo · Repository
- Meander morphology classifier: Launch demo · Repository
These demos use public-safe synthetic or example data so the workflows can be explored directly in the browser without installing the repositories locally.
| Project | Area | What it demonstrates |
|---|---|---|
Meander Morphology Classifier |
Scientific ML / geomorphology | CWT spectra, autoencoder latent spaces, clustering, Streamlit workflows, Zenodo-linked models, peer-reviewed research, and a one-click demo |
Hydraulic Digital Twin |
Digital twins / industrial AI | Synthetic hydraulic sensor data, validation checks, anomaly detection, operating-state classification, and decision-support reports |
Structural Audio Anomaly Detection |
Applied ML / anomaly detection | Signal processing, time-frequency features, latent representations, anomaly screening, and reproducible structural-test workflows |
Urban Drainage Sensor Data Toolkit |
Environmental infrastructure / sensor QA/QC | Public-safe telemetry QA/QC, synthetic drainage-monitoring data, automated reports, anomaly screening, map outputs, and a one-click demo |
LDSFL Meander |
Scientific computing / hydrology | Reduced morphodynamic modelling, reproducible simulations, CLI/GUI workflows, documentation, and citation metadata |
TDMS Sync Checker |
Engineering data QA/QC | Timing and synchronisation checks, split-file continuity, inactive-channel detection, diagnostics, and report generation |
- Full-scale tidal blade testing:
tidal-blade-test-analysis— public-safe research-software workflows for composite tidal-blade structural-test data, including TDMS inspection, static response, fatigue-cycle summaries, natural-frequency helpers, actuator checks, and applied-AI screening.
This repository complements the main selected projects by showing how private experimental data can be converted into a public-safe and reproducible engineering-analysis workflow.
-
Remote sensing / environmental monitoring:
strandings_from_space— collaborative research software for very-high-resolution satellite-image pre-processing, annotation, and observer-count comparison for cetacean strandings. My fork is available atsergioald/strandings_from_space. -
Open-source research software / deep learning:
GeoOcean/BlueMath_tk— upstream contributions to the deep-learning autoencoder module of a climate-data analysis toolkit.
- Applied AI: anomaly detection, classification, time-series and signal features, model validation
- Scientific ML: autoencoders, latent spaces, clustering, spectral features, model interpretation
- Engineering data: sensor networks, TDMS files, synchronisation diagnostics, data-quality checks
- Environmental data: hydrology, hydraulic modelling, urban drainage, remote sensing, environmental monitoring
- Scientific Python: NumPy, pandas, SciPy, Matplotlib, scikit-learn
- Research software: reproducible workflows, command-line and GUI tools, documentation, testing, and public-safe examples
I try to make repositories useful as engineering and research artefacts, not just as code.
Where possible, projects include:
- a clear problem statement;
- installation and usage instructions;
- tests and reproducible examples;
- synthetic or public data;
- visual outputs and reports;
- explicit assumptions, limitations, and validation boundaries;
- citation metadata where relevant.
This matters most when real industrial, environmental, or research data cannot be published. In those cases, I build a synthetic or public-safe version that still demonstrates the underlying workflow.
I'm interested in applied AI, research software, scientific machine learning, digital twins, sensor-data QA/QC, and environmental and industrial monitoring.
- Portfolio: sergioald.github.io
- GitHub: @sergioald
- LinkedIn: Sergio Lopez Dubon
- Academic profile: University of Edinburgh Research Explorer
- Publications: Google Scholar
- ORCID: 0000-0003-0663-607X