About me

Hello there! I am a physician-scientist in training, working in the interdisciplinary field of Computational Pathology. I obtained my MBBS (also known as MD in the US) from Cairo University, and my PhD in Computer Science and Informatics from Emory University in Atlanta, GA under the supervision of Dr. Lee A.D. Cooper.

I am told that I'm good at singing, but don't worry, I'm not annoying at karaoke parties! :) I've moved a lot since childhood. I spent my elementary school in Saudi Arabia, my high school at Cambridge High School in Abu Dhabi, UAE, my medical school in Cairo, Egypt, with a gap research year at OIST in Okinawa, Japan, and my graduate school in Atlanta and Chicago, USA. On my free time, I love to read (mostly non-fiction) in the fields of philosophy, economics, and technology. I am fluent in English and Arabic.

Key skills

I am a physician by training, and have some exposure to anatomical pathology, particularly breast cancer, through my PhD work. I look forward to formalizing and significantly expanding my knowledge in pathology through residency.

In data science, I mainly work with the following tools/languages:

  • Programming (*, proficient): Python*, MATLAB*, SQL*, Bash, R, JAVA.

  • Noteworthy python libraries: tensorflow, keras, pytorch, openslide.

  • Miscellaneous: Linux, git, docker, RESTful API, Latex.


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What do I do?

My dissertation work utilizes crowdsourcing (engaging non-experts to produce data) and machine vision to discover prognostic histologic and genomic elements in breast cancer. A binding theme is the curation of datasets and tools to enable the development of "explainable" models that are amenable to understanding by pathologists and oncologists. I also have a long-standing interest in medical education, mentorship, medical entrepreneurship, and open science.

For more details, feel free to explore the rest of this site or to take a look at my C.V. here. (last updated: July, 2021).

Gif of region and cell segmentation overlays
Sample tissue type predictions from our deep learning models (breast cancer, TCGA)

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