Blog

Thoughts on machine learning, chemical engineering, data science in industry, and the intersection of physical sciences with modern computation.

Your ML Validation May Be Misleading You: Random Splits of Pipeline Data

Pipeline data is spatially autocorrelated. Random train-test splits can make machine-learning models look more reliable than they are.

draft pipeline-integrity machine-learning spatial-autocorrelation IPC

The Cost of a Half Percent

When is a 0.5% discrepancy good enough?

draft pipeline-integrity uncertainty radon science

Why grad school mattered: Blasius, boundary layers, and the value of understanding

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draft fluid-mechanics boundary-layer blasius grad-school engineering fitness-for-service

Estimating pi with Sobol sequence sampling

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draft sampling sobol monte-carlo pi

400,000 years or Better Models

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draft risk pipelines uncertainty