Engineering education and research
Scientific computing · AI/ML · Digital twins · Heat and mass transfer · Statistical quality
I build transparent, reproducible tools for engineering analysis and education. My work connects computational methods with physical systems, process data, and practical decision-making.
- SPC Connect — a Shiny for Python application for X̄-R control charts, revised control limits, process-capability indices, CSV/Excel workflows, and auditable statistical analysis.
- Thermal Digital Twin — a first-order thermal model with synthetic observations and transparent parameter estimation for digital-twin and thermal-engineering experimentation.
- Engineering Data Quality — a non-destructive Python toolkit for profiling and validating subgrouped engineering data before SPC analysis.
- Heat and Mass Transfer Models — inspectable analytical models for transient thermal behavior and engineering education.
- Scientific Computing ROCm Diagnostics — conservative CPU/CUDA/ROCm environment diagnostics for reproducible accelerated-computing experiments.
- Engineering Experiment Design — transparent two-level factorial design generation and coded effect estimation.
- Scientific machine learning and AI-assisted engineering
- AI/ROCm experimentation and reproducible computational workflows
- Digital twins for engineering systems
- Heat and mass transfer
- Statistical process control and quality engineering
- Design of experiments and engineering data analysis
- Engineering education through inspectable software
- Credly credential — public digital credential.
- Affiliation: Universidad Paraguayo Alemana (UPA)
- ORCID: 0009-0004-6275-3013
- Location: Paraguay
The public collection is intentionally organized around evidence-based projects in engineering computation, AI/ML, digital twins, thermal systems, experimental design, and statistical quality.