Who am I?

Hello there! I am an incoming PGY-1 Pathology Resident in the AP-PSTP track at Northwestern University, Feinberg School of Medicine. I am an aspiring academic pathologist, working in the interdisciplinary field of Computational Pathology. I obtained my MD 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. I've permanently relocated to the US since starting my graduate school in late 2016, having spent the most time in Atlanta, Chicago, and a couple of summers in San Jose. On my free time, I love to read/listen to books & podcasts on philosophy, economics, science, and technology.  I am fluent in English and Arabic. 

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What do I want?

I aspire to be an Academic Pathologist. Pathologists have some of the world’s most exciting, challenging, and intellectually stimulating jobs. They are clinicians, scientists, and pattern recognition gurus all at once. The clinical and research aspects of this dual role would be synergistic. Constant exposure to research papers and conferences would keep me abreast of new updates and help me deliver cutting-edge care. On the other hand, pathology practice would guide my inquiry, allowing me to focus on relevant research with a clear pathway to clinical translation. 

What skills do I have?

I am a physician by training and have had exposure to anatomical pathology through my PhD. I look forward to significantly expanding my knowledge in pathology through residency. Besides teaching and academic research, I am comfortable with these data science tools:

My PhD research in a nutshell

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.

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

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