Mohamed Amgad
Pathology Resident Physician
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolMohamed Amgad is a pathology resident at Northwestern University who bridges clinical practice with computational pathology, holding an MD from Cairo University and a PhD in Computer Science/Biomedical Informatics from Emory. With nine years of experience spanning wet-lab research, deep-learning development at Ventana, and academic roles from visiting fellow to postdoc, he applies AI to diagnostic pathology and personalized medicine. His work synthesizes molecular biology background (OIST) with high-performance biomedical informatics, enabling translational projects that move models toward clinical utility. Based in Chicago, he combines rigorous academic training (PhD GPA 3.95) with hands-on pathology residency, uniquely positioning him to translate algorithmic advances into real-world diagnostic workflows.
9 years of coding experience
12 years of employment as a software developer
High School, Cambridge University IGCSE (International General Certificate of Secondary Education) and A-levels, High School, Cambridge University IGCSE (International General Certificate of Secondary Education) and A-levels at The Cambridge High School, Abu DhabiDoctor of Philosophy (Ph.D.), Computer Science (Biomedical Informatics), 3.95 / 4.0, Doctor of Philosophy (Ph.D.), Computer Science (Biomedical Informatics), 3.95 / 4.0 at Emory UniversityBachelor of Medicine, Bachelor of Surgery (MBBS), Medicine, Excellent with Honors, Bachelor of Medicine, Bachelor of Surgery (MBBS), Medicine, Excellent with Honors at Cairo University
English, Arabic
Github Skills (58)
image-analysis10
biology10
histopathology10
python10
data-science10
image-processing10
medical-image-processing10
doi10
machine-learning10
wsi10
ml10
bids10
keras10
deep-learning10
tensorflow10
Programming languages (4)
Github contributions (5)
PathologyDataScience/BCSS
Aug 2019 - Mar 2022
Use this to download all elements of the BCSS dataset described in: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083
Contributions:74 commits, 2 PRs, 72 pushes in 2 years 8 months
doicooperstructuredsemantic-segmentationelements
cooperlab/KNN_Survival
Dec 2016 - Dec 2017
Contributions:319 commits, 314 pushes, 1 branch in 1 year
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