I build applied AI and research-software tools for engineering systems, with a focus on:
- Digital twins
- Anomaly detection
- Sensor data analysis
- Scientific Python workflows
- Reproducible engineering software
My work sits at the intersection of machine learning, data quality, simulation, and research software engineering.
I am especially interested in using software and AI to make engineering systems easier to monitor, analyse, and understand.
Current focus areas:
- Applied AI for engineering data
- Anomaly detection from sensor and acoustic signals
- Digital twin prototypes
- Hydraulic and environmental modelling tools
- Reproducible Python packages and research workflows
| Project | What it demonstrates |
|---|---|
| audio-anomaly-detection-structural-testing | Applied machine learning for anomaly detection using audio/sensor data, with reproducible research-software structure |
| synthetic-hydraulic-digital-twin-demo | Digital twin concepts, synthetic sensor data, monitoring workflows, and applied ML for engineering systems |
| LDSFL_Meander | Scientific modelling software with CLI/GUI workflows, documentation, citation metadata, and reproducibility practices |
| tdms-sync-checker | Engineering data QA/QC tooling for checking synchronisation issues in measurement files |
Languages and tools
- Python
- Scientific Python: NumPy, pandas, SciPy, Matplotlib
- Machine learning: scikit-learn and applied ML workflows
- Testing and packaging: pytest, pyproject.toml, CI workflows
- Documentation: Markdown, MkDocs/Sphinx-style project docs
- Data workflows: sensor data, TDMS files, synthetic datasets, reproducible examples
Engineering and research themes
- Structural and acoustic anomaly detection
- Digital twins for hydraulic and infrastructure systems
- Signal processing and sensor data validation
- Reproducible computational research
- Research software engineering
I try to make my repositories useful not only as code, but as understandable engineering artefacts.
I aim to include:
- Clear README files
- Reproducible examples
- Tests
- Documentation
- Project structure suitable for reuse
- Licenses and citation metadata where relevant
- Visual outputs or diagrams to explain results
I am developing my portfolio around the theme:
Applied AI and research for engineering systems.
The goal is to build tools that connect data, models, and domain knowledge so that complex systems can be monitored and analysed more effectively.
- GitHub: @sergioald
- LinkedIn: LinkedIn
- Portfolio: Coming soon