Haitham Elmarakeby
Instructor at Broad Institute of MIT and Harvard
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolHaitham Elmarakeby is an instructor and computational scientist with 11 years of experience developing interpretable, biologically informed machine learning models that integrate molecular and clinical data to advance oncology and precision medicine. Based in Boston, he leads translational modeling and data-driven biomarker discovery at Dana-Farber, Harvard Medical School, and the Broad Institute, translating molecular AI into therapeutic insights and treatment outcome predictions. His work emphasizes mechanistic interpretability—bridging deep learning with biological plausibility—to uncover disease mechanisms and accelerate drug discovery. Trained with a PhD from Virginia Tech and a background in software architecture and systems engineering, he brings rare fluency across production-grade software, large-scale data analysis, and bench-to-bedside research.
11 years of coding experience
6 years of employment as a software developer
BSc, Computer Engineering, BSc, Computer Engineering at Al-Azhar UniversityPhD, Computer Science, PhD, Computer Science at Virginia TechMSc, Computer Engineering, MSc, Computer Engineering at Cairo University
English, Arabic
Github Skills (5)
sparse10
classification10
scipy7
neural-network6
python1
Programming languages (3)
Github contributions (5)
marakeby/pnet_prostate_paper
Oct 2020 - Nov 2021
P-NET, Biologically informed deep neural network for prostate cancer classification and discovery
Contributions:1 release, 46 commits, 32 pushes in 1 year
classificationneural-networksparseexplainabilitycancer
marakeby/clinicalNLP2
Mar 2021 - Sep 2023
Contributions:1 PR, 17 pushes in 2 years 6 months
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