Research made concrete.

These projects connect machine-learning questions with the data, workflows, and evaluation practices needed to answer them responsibly.

In development

Reproducible research workflows

Practical tooling for moving from raw scientific data to repeatable experiments and inspectable results.

Data pipelinesAutomationExperiment tracking
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Ongoing

Model evaluation and diagnostics

Evaluation practices that make model comparisons clearer, more honest, and more useful than a single headline metric.

DiagnosticsError analysisModel comparison
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