Zeyad Emam is a Senior ML Researcher with eight years of experience bridging rigorous applied mathematics and production machine learning, currently at Apple in San Francisco. He is a PhD candidate in Computational and Applied Mathematics at UMD and a research fellow at NIH, where he applies ML to biomedical image segmentation and has published work on 3D electron microscopy and active learning. At Apple he shipped eye-tracking for Vision Pro and now works on egocentric multimodal LLMs, combining systems-level product impact with deep research. His academic work explores multi-scale transform methods to harden CNNs against adversarial attacks, reflecting a rare blend of theoretical signal-processing insight and practical, deployable ML.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Aerospace Aeronautical and Astronautical Engineering, Bachelor of Science - BS Aerospace Aeronautical and Astronautical Engineering at University of Maryland - A. James Clark School of Engineering
French Baccalauréat, French Baccalauréat at Lycée Töpffer
Doctor of Philosophy - PhD Computational and Applied Mathematics, Doctor of Philosophy - PhD Computational and Applied Mathematics at University of Maryland
Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.
Contributions:4 commits, 2 PRs, 2 pushes in 4 days
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