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CORE COMPETENCIES

Tech stack, methods, and analytical strengths behind Michele’s work

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.

Tech Stack & Core Competencies

8 CAPABILITIES

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.

Python SQL Excel pandas NumPy Data Cleaning Exploratory Analysis Feature Engineering
7 CAPABILITIES

Machine Learning & Evaluation

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.

scikit-learn XGBoost TensorFlow/Keras Time-Series Modelling Anomaly Detection Model Calibration Zero-Leakage Validation
7 CAPABILITIES

Predictive & Reliability Analytics

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.

RUL Estimation Predictive Maintenance Condition-Based Maintenance Weibull Analysis Fleet-Health Analytics Drift Detection (PSI) Gradio
11 TOOLS

MLOps, Serving & Deployment

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.

MLflow DVC FastAPI Pydantic Docker Kubernetes GitHub Actions CI/CD AWS ECR/EKS Jupyter Git/GitHub VS Code
8 METHODS

Project & Research Methods

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.

Physics-Aware Features Conformal Intervals Calibrated Risk Bands PiNet Benchmarking MobileNetV2 Siamese Networks Triplet Loss Bayesian Optimisation
8 STRENGTHS

Engineering & Operations

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.

Civil Engineering Structural Analysis Impact Loading Analysis Cost Analysis Logistics Coordination Scheduling & Routing Resource Allocation Stakeholder Coordination
INTERACTIVE MAP

Competencies Mapped to Experience

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.

Moon Shot Moon Shot