Mark Ibrahim
Staff Research Engineer at Capital One's Center for Machine Learning, Explainable AI Team
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMark Ibrahim is a Staff Research Engineer at Meta's FAIR with 11 years of experience building production-grade ML systems and explainability tooling. He leads explainable AI efforts at Capital One and advises Insight Data Science fellows, bridging deep research with pragmatic engineering. His open-source contributions to FacebookResearch's CrypTen highlight expertise in privacy-preserving ML—implementing and stabilizing autograd, conv gradients, and batch-norm for secure training. Trained in applied mathematics and statistics (MS, MicroMasters) and with early quant finance experience, he blends rigorous mathematical intuition with software delivery. He has a track record of turning research prototypes into scalable pipelines and developer-friendly tools, from distributed Spark/Neo4j systems to production debugging of complex ML gradients. Based in New York, he pairs academic depth with hands-on implementation across research and enterprise environments.
11 years of coding experience
1 year of employment as a software developer
Political Science and Government, Political Science and Government at Sciences PoNSF Summer Research In Pure Mathematics, NSF Summer Research In Pure Mathematics at University of ChicagoMicroMasters, Statistics, MicroMasters, Statistics at Massachusetts Institute of TechnologyMaster of Science (M.S.), Applied Mathematics, Master of Science (M.S.), Applied Mathematics at University of VermontBachelor's degree magna cum laude, Mathematics with Honors, Bachelor's degree magna cum laude, Mathematics with Honors at Hamilton College
French, English
Github Skills (9)
pytorch10
machine-learning10
python10
autograd10
batch-normalization9
convolutional-neural-networks9
faster-rcnn8
mask-rcnn8
testing8
Programming languages (5)
Github contributions (5)
facebookresearch/CrypTen
Oct 2019 - Apr 2021
A framework for Privacy Preserving Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 64 commits, 38 PRs in 1 year 6 months
Contributions summary:Mark made multiple contributions focused on improving and extending the functionality of the CrypTen framework for privacy-preserving machine learning. They implemented and debugged batch normalization, including the backward gradients, and fixed issues related to conv2d and conv1d gradients. The user also refactored and enhanced testing for autograd and gradient operations, and contributed to the integration of model and function benchmarks. They improved existing approximations.
privacydeep-learningprivacy-preserving-machine-learningdifferential-privacymachine-learning
marksibrahim/math
Mar 2015 - Oct 2017
Contributions:55 commits, 41 pushes, 1 branch in 2 years 6 months
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