Amil Khan
Teaching Assistant ECE 281 Advanced Computer Vision at UC Santa Barbara
Goleta, California, United States
Summary
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Rockstar🎓
Top SchoolAmil Khan is a PhD student in Electrical and Computer Engineering at UCSB who designs and ships large-scale image and video processing systems, blending research in novel computer vision algorithms with production ML infrastructure. With nine years of experience and over five years building production-grade CV/ML software plus three years managing teams, he leads BisQue—an NSF-funded platform for terabyte-scale imaging, low-latency ML microservices, and up-to-5D visualization. He’s equally comfortable authoring papers on supervised and unsupervised vision methods and operating Kubernetes-backed microservices using Kafka, Minio, and Argo at production scale. A practiced debugger and educator (TA for multiple ECE vision courses), he also consults on ML infrastructure for the Allen Institute, bringing both academic rigor and hands-on deployment expertise. Notably, his background in applied statistics and materials research gives him a cross-disciplinary perspective that informs robust, data-driven system design.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Electrical and Computer Engineering, Doctor of Philosophy - PhD Electrical and Computer Engineering at UC Santa Barbara
Stackoverflow
1reputation
0reached
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Github Skills (57)
threejs10
geometry10
image-processing10
gpu-programming10
manifold10
geometric10
nvidia10
bioinformatics10
medical-imaging10
computer-vision10
cloud-computing9
visualization9
kubernetes9
python9
machine-learning9
Programming languages (5)
Github contributions (5)
amilworks/GanDetection
Apr 2019 - Jan 2023
Contributions:2 releases, 9 commits, 8 pushes in 3 years 9 months
amilworks/BisQue_CompositeStrength
Dec 2018 - Apr 2023
Predicts effective yield strength of a composite given its 3-D microstructure
Contributions:1 release, 20 pushes, 5 branches in 4 years 4 months
sklearnmicrostructureeffectivemachine-learningstrength
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