// ENGINEERING CASE STUDIES
Post-mortems that show how production AI actually works.
Each study follows the same discipline: context → problem formulation → mathematical strategy → production architecture → concrete business ROI. Filter by ML discipline.
Defect Diagnosis & Root Cause Rule Engine
End-to-end AI system diagnosing mill defects and extracting decision-tree rules to guide plant operators and production scheduling — replacing manual recipe tuning with deterministic, transparent guidance.
Data Scientist · Noodle.ai PQAI
Read studySteel Coil Mechanical Property Precognition
Machine learning and deep learning models predicting Yield Strength, Tensile Strength, and Elongation from alloy composition and process recipes — enabling recipe simulation to slash quality variability.
Data Scientist · Noodle.ai PQAI
Read studySpurious Outlier Detection for High-Frequency Time Series
An integrated, scalable approach to detecting spurious outliers in high-frequency sensor streams, differentiating contextual anomalies from noisy outliers to improve the reliability of analytical models on manufacturing IoT data. Subject of a published US patent (US20210294787A1).
Data Science Intern · Noodle.ai
Read studyPredictive PACS Latency Monitoring Pipeline
Built an ELK Stack-powered predictive monitoring pipeline for PACS (Picture Archiving and Communication System) that forecasts medical image transfer latency using decision-tree models — enabling proactive IT triage before clinicians feel impact.
R&D Intern · Philips Innovation Campus
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