Ian Greenhoe
Senior Software Engineer at Fanatics
Saint Petersburg, Florida, United States
Summary
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Rockstar🎓
Top SchoolIan Greenhoe is a Senior Software Engineer with 15 years of experience specializing in computer vision, robotics, and algorithm design, currently at Fanatics in Saint Petersburg, FL. He has led algorithm and image-science teams (including principal roles at Leidos) to deliver production-grade 3D x‑ray analysis, real‑time vision systems, and manufacturing inspection pipelines. Ian blends deep math (BS in Mathematics) with systems-level engineering—shipping SIMD-optimized vision code, Node.js and Rust performance improvements, and embedded/CI systems managing 300k+ images. He’s an active ML practitioner and open-source contributor with hands-on work on GANs and adversarial-example tooling (including privacy-focused GAN variants and CleverHans contributions). Comfortable moving between C/C++, MATLAB, Python, and full-stack tooling, he repeatedly turns research-grade algorithms into robust, deployable products. A less obvious thread through his career is a knack for measurable performance wins: reducing user-facing delays from minutes to seconds and shaving runtime percentiles through targeted algorithmic and engineering changes.
15 years of coding experience
13 years of employment as a software developer
BS Mathematics, BS Mathematics at University of Washington
Spanish, English
Stackoverflow
11reputation
0reached
1answer
0questions
Github Skills (20)
python10
dcgan10
machine-learning10
cgan10
generative-adversarial-network10
numpy10
security10
adversarial-machine-learning10
tensorflow10
cyclegan10
theano10
benchmark9
benchmarking9
paper9
pytorch8
Programming languages (6)
Github contributions (5)
goodfeli/adversarial
Jun 2014 - Jun 2018
Code and hyperparameters for the paper "Generative Adversarial Networks"
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 1 PR, 1 push in 4 years
Contributions summary:Ian's contributions focused on modifying and extending the code for a Generative Adversarial Network (GAN). They adapted code and hyperparameters, fixed issues with data handling, and worked on likelihood evaluation methods. The changes included alterations to core model components, likelihood calculations, and MNIST-specific implementations, revealing a focus on practical application and refinement of the GAN model.
pytorchdeep-learningadversarialhyperparametersneural-networks
cleverhans-lab/cleverhans
Sep 2016 - Mar 2019
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Role in this project:ML Engineer
ML EngineerContributions:1 release, 918 commits, 503 PRs in 2 years 6 months
Contributions summary:Ian contributed code copied from another repository, adding and modifying TensorFlow-based utilities for constructing and evaluating adversarial examples within the CleverHans framework. The contributions focused on implementing and training models and developing techniques for machine learning security. The changes included adding a code of conduct and implementing a fix for NotImplemtedError to support and maintain CleverHans framework.
benchmarkingrobustnessadversarial-machine-learningsecurityadversarial
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