Zahoor Zafrulla
Senior ML Engineer- Computer Vision at Acubed
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolZahoor Zafrulla is a Principal Engineer in Mountain View with a decade of experience applying machine learning to computational biology, computer vision, and human–computer interaction. His career spans academia and industry—from a PhD at Georgia Tech and ML research at Stanford Medicine to product-focused roles at 10x Genomics, Planet, and Airbus—bridging foundational research and production engineering. He specializes in gesture and pattern recognition, bringing deep expertise in applied vision systems and biology-informed ML models. Zahoor has a track record of moving prototypes into deployed systems across robotics, remote collaboration, and genomics workflows, and often works at the intersection of sensors, 3D data, and HCI. Notably, his background includes building robust hand-tracking and wearable-camera localization systems early in his career, reflecting a long-standing practical focus on real-world sensing challenges.
10 years of coding experience
17 years of employment as a software developer
Bachelor's degree Information Science & Engineering, Bachelor's degree Information Science & Engineering at Visvesvaraya Technological UniversityDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Georgia Institute of TechnologyMaster's degree Computer Science, Master's degree Computer Science at National University of Singapore
English, Urdu, Hindi, Kannada
Github Skills (16)
nexus9
rna-seq9
seq9
exo8
chip-seq7
batch7
deep-learning5
api4
predict4
loss4
genomics3
zoo3
loss-functions3
keras2
accessibility1
Programming languages (3)
Github contributions (5)
kundajelab/genomics-DL-archsandlosses
Jun 2021 - Jul 2022
A collection of Deep Learning architectures and loss functions from across the genomics literature
Contributions:1 release, 2 PRs, 30 pushes in 1 year 1 month
architecturesloss-functionsdeep-learninggenomicsliterature
kundajelab/mseqgen
Jun 2020 - Aug 2022
Multi task batch generator for training deep learning models on CHIP-seq, CHIP-exo, CHIP-nexus, ATAC-seq, RNA-seq (or any other -seq)
Contributions:8 releases, 1 review, 107 commits in 2 years 2 months
multi-taskchip-seqbatchdeep-learningbatch-task
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