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ocansey/README.md

Isaiah Thompson Ocansey, Ph.D.

Data Scientist | Machine Learning Engineer El Paso, Texas

Ph.D. in Data Science. I build compact machine learning models that stay cheap enough to deploy and honest enough to trust — anomaly detection, efficient LLMs, and the monitoring that catches models when they decay.

  • LogTinyLLM — compact transformer, 98.83% anomaly-detection accuracy at ~70% lower compute cost than large-LLM baselines (arXiv:2507.11071)
  • Drift Arena — live model monitoring: PSI drift detection, McNemar-gated promotion
  • The Label Budget — three ML algorithms from first principles, no libraries

Links: Portfolio · Google Scholar · LinkedIn

Python PyTorch SQL R Machine Learning Deep Learning LLMs MLOps

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  1. drift-arena drift-arena Public

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  2. label-budget label-budget Public

    How many labels does supervised intrusion detection need before it beats an unsupervised model that needs none? A live in-browser experiment — Isolation Forest, gradient boosting and an RBF-SVM, al…

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  3. portfolio portfolio Public

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  4. student-outcome-early-warning student-outcome-early-warning Public

    Capacity-constrained early warning for student withdrawal and failure, from registrar data alone. Evaluated on future cohorts, scored by precision at adviser capacity and lead time.

    Python 1

  5. multimodal-insider-threat multimodal-insider-threat Public

    Python 1

  6. pharos-rx pharos-rx Public

    Python 1