Selected work

Case studies, not bullet points.

Problem, data, model, deployment, result — written for engineers as much as for recruiters. Each page says exactly what I did myself, and what I would do differently.

Written from projects delivered at thyssenkrupp Automotive, Industrie Saleri Italo and Politecnico di Milano. Confidential details, data and imagery are deliberately omitted.

01 2023 — 2026 · Manufacturing quality

Semi-supervised vision on the line

Computer Vision for Assembly Line Quality Control

A camera system on the assembly line that detects foreign objects and defects in real time, trained semi-supervised because a labelled defect dataset did not — and could not — exist.

Computer VisionDeep LearningAnomaly Detection
Approach
Semi-supervised
Detection
Real time
Target
Foreign objects
Defect labels
Not required
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02 2023 — 2026 · Process monitoring

5 million datapoints a day

Anomaly Detection at Line Scale

An end-to-end anomaly detection pipeline for manufacturing process data, with an optimisation framework that evaluated 48 000 model variants against performance metrics designed for the actual production question.

Anomaly DetectionTime SeriesMLOps
Throughput
up to 5M points/day
Variants evaluated
48 000
Latency
Low, near real time
Metrics
Custom designed
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03 2024 — 2025 · Precision assembly

Physics and statistics, together

Hybrid Physics / Data-Driven Assembly Model

A model that keeps the physical description of the assembly and learns what it cannot capture, reducing scrap and rework rates in assembly operations by 10 %.

RegressionHybrid ModellingProcess Optimisation
Scrap & rework
−10 %
Formulation
Physics + ML
Model search
AutoGluon
Fallback
Physical model
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04 2024 — 2025 · Knowledge & support

Generative AI that stays in the building

On-Premise RAG Assistant for the Company Wiki

A locally hosted retrieval-augmented chatbot over the internal wiki, plus a benchmarking framework to compare LLMs on company-relevant prompts and data rather than on public leaderboards.

LLMsRAGGenerative AI
Hosting
Fully on-premise
Source
Company wiki
Benchmark
Company prompts
Data leaving site
None
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05 2024 · Process understanding

Modelling what you do not know

Bayesian Network for Uncertainty Quantification

A probabilistic graphical model mapping the dependencies between process parameters, with Monte Carlo simulation used for rigorous uncertainty quantification and robust system optimisation.

Probabilistic ModellingUncertainty QuantificationOptimisation
Model
Bayesian network
Sampling
Monte Carlo
Output
Full distributions
Use
Robust optimisation
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06 2023 — 2024 · Design optimisation

Optimisation in the loop with the solver

Genetic Algorithms Coupled to FEM

A global optimisation framework using genetic algorithms, coupling Python directly to Abaqus to automate synthetic data generation and accelerate design convergence.

OptimisationSimulationMechanical Design
Solver
Abaqus FEM
Search
Genetic algorithm
Data
Synthetic, automated
Goal
Faster convergence
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07 2019 — 2020 · Structural health monitoring

MSc thesis · peer-reviewed publication

Load Monitoring for Aeronautical Structures

An inverse–direct approach for load and strain monitoring in aeronautical structures, based on a calibration matrix, verified both numerically and experimentally — and published in Structural Control and Health Monitoring.

Structural AnalysisInverse ProblemsExperimental Validation
Publication
SCHM journal
Method
Inverse–direct
Verification
Numerical + experimental
Thesis grade
110/110
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Contact

Currently looking for the next problem worth solving.

Industrial AI, manufacturing data, computer vision, generative AI — or anything where engineering and machine learning have to meet in production. If that sounds like your team, I would like to hear about it.

Mauren, Liechtenstein