Insights
Long-form writing on graph machine learning, anomaly detection, analytics engineering, data systems and responsible AI — focused on methods, trade-offs and practical reasoning.
A practical guide to graph anomaly detection with graph autoencoders, attention-based encoders, anomaly scoring, evaluation pitfalls and structure-aware regularization.
Read article →A practical blueprint for retail analytics engineering: validation, dimensional modeling, incremental processing, testing and trustworthy business metrics.
Read article →A practical framework for moving responsible generative AI from policy statements into course design, assessment, academic integrity and continuous evaluation.
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