Austin Blanton
Philadelphia, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolAustin Blanton is a full-stack developer with 13 years of experience building data-driven, production-grade systems and interactive front-ends. He’s worked across startups and large media at The Washington Post, where he built real-time recommendation APIs, scaled services for millions of daily users, and shipped a hackathon-winning LLM-powered article-visualization. His background in computer vision and biometrics includes substantial open-source contributions to OpenBR, adding dataset support, optical flow and HOG/HoF pipelines, and performance optimizations. Austin pairs machine learning and data engineering skills—he designed automated COVID data pipelines and anomaly detection for newsroom use—with hands-on front-end craft, from audio-player UIs to high-traffic publishing platforms. Known for pragmatic productionization (Tornado, AWS autoscaling, Datadog) and playful engineering experiments (in-browser ffmpeg via WebAssembly), he bridges research prototypes and resilient services. Based in Philadelphia, he brings a blend of academic rigor and product-focused execution rooted in a UVA computer science education.
14 years of coding experience
11 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Virginia
Stackoverflow
300reputation
34kreached
2answers
5questions
Github Skills (17)
opencv10
c-language10
image-processing10
video-processing10
optical-flow10
computer-vision10
cprogramming-language10
template-matching9
sift9
machine-learning7
content-type6
has-many6
mod-wsgi6
python6
dataframe6
Programming languages (6)
Github contributions (5)
biometrics/openbr
Jul 2013 - Mar 2016
Open Source Biometrics, Face Recognition
Role in this project:
Back-end Developer
Back-end DeveloperContributions:235 commits, 2 PRs, 21 pushes in 2 years 8 months
Contributions summary:Austin primarily contributed to the development of the OpenBR biometrics project, focusing on implementing and integrating various computer vision algorithms. They added support for the KTH dataset, including a script for downloading and preprocessing data. Furthermore, they worked on integrating optical flow algorithms, adding new features and parameters, and optimizing performance, including creating the HoF and HOG pipeline. They also implemented new transforms such as a segmentation transform, and corrected code to reflect improvements from refactoring.
biometricsface-recognition
imaus10/cascade
May 2020 - Nov 2022
Layer a live group musical performance part by part in the web browser using WebRTC (batteries not included)
Contributions:34 pushes, 2 branches in 2 years 7 months
peerjsbrowserwebtransportbatterieswebrtc
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