Data Analysis & Programming
Data preparation, exploration, and feature development.
The current CV combines core programming and analytical tooling with practical data cleaning, exploratory analysis, and feature engineering.
This page pulls together the detailed tools, methods, and competency areas that sit underneath Michele’s Curriculum Vitae, with particular emphasis on predictive maintenance, anomaly detection, time-series analysis, and engineering-grounded machine learning delivery.
Data preparation, exploration, and feature development.
The current CV combines core programming and analytical tooling with practical data cleaning, exploratory analysis, and feature engineering.
Model development with disciplined validation and calibration.
Applied machine learning across classical and neural frameworks, with particular attention to time-series behaviour, anomaly detection, calibration, and leakage control.
Asset-health estimation and maintenance decision support.
Reliability-focused analytics spanning RUL estimation, maintenance strategy, fleet-health monitoring, distributional analysis, drift detection, and interactive delivery.
Experiment tracking, API serving, containers, CI/CD, and cloud deployment.
A deployment-oriented workflow covering reproducibility, typed serving interfaces, container orchestration, automated delivery, and AWS infrastructure.
Evidence-backed methods demonstrated in FusionCore and the MSc thesis.
The project record combines physics-aware feature design, calibrated uncertainty, hybrid model comparison, few-shot learning, metric learning, and optimisation.
Physical-system grounding combined with long-term operational delivery.
Civil engineering and site delivery experience sit alongside cost analysis, logistics, scheduling, routing, resource allocation, and stakeholder coordination.
This dedicated holographic map links Michele's core competencies and technical stack directly to the experiences where they were developed, tested, or operationalised. It is designed as a cleaner, more spatial way to inspect how methods, tools, and engineering strengths connect across FusionCore v0, FusionCore v1, PiNet, the MSc thesis, the Moon Landing platform, and earlier engineering work.
Open the map to rotate the network, hover nodes for quick previews, and click a node to surface a holographic readout showing what that competency or tool is tied to in practice.