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  • Gilead Sciences
  • Boulder, CO

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mazin-abdelghany/README.md

I am a physician-scientist dual-trained in infectious diseases and critical care medicine. I practice medicine and design and run clinical trials as a Director, Clinical Research at Gilead Sciences in Foster City, CA.

I cultivated a special interest in machine learning while completing my post-doctoral fellowship at the Unviersity of California, San Francisco (UCSF) in Chaz Langelier's lab. Using various machine learning techniques applied to metagenomic sequencing, our team was able to improve the prediction of life-threatening infections in the intensive care unit.

Though all my previous work was done in R, I am currently learning Python to expand my machine learning toolkit.

I can be reached by email at mazinabdelghany@duck.com and welcome a discussion with folks looking to collaborate on projects involving machine learning and/or medicine!

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  1. differential-gene-expression-sepsis-classification differential-gene-expression-sepsis-classification Public

    Post-doctoral work on sepsis classification applying supervised and unsupervised machine learning models to differential gene expression data.

    RMarkdown

  2. coursera-machine-learning coursera-machine-learning Public

    Notes on Andrew Ng's Coursera course including best practices and common pitfalls and my derivation of the normal equation using matrix calculus.

    TeX

  3. kaggle-learning kaggle-learning Public

    A repository to practice machine learning with Python using Kaggle datasets.

    Jupyter Notebook

  4. odin-foundations odin-foundations Public

    The initial Foundations material for the Odin Project.

    HTML

  5. machine-learning-in-R machine-learning-in-R Public

    Assignments for the UCSF Machine Learning in R course.

    RMarkdown

  6. leetcode-solutions leetcode-solutions Public

    A repository of my solutions to LeetCode problems as I learn Python.

    Python