Harold Hausman
Principal Software Engineer at TechAscent, LLC
Boulder, Colorado, United States
Summary
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Rockstar🎓
Top SchoolHarold Hausman is a Principal Software Engineer with 18 years of experience building cloud-native, full-stack systems that bridge math, machine learning, and artist-focused tooling. Based in Boulder, he combines deep classical mathematics training with autodidactic fluency in Clojure, C/C++, JavaScript, and graphics pipelines to deliver end-to-end products on time and under budget. He has driven ML work in open-source Clojure projects (notably improving neural network training and loss testing) and led efforts that turned research-grade models into profitable business features for clients like Condé Nast. His background includes shipping large-scale graphics and content tools at NVIDIA and creating web-based educational programming environments used by hundreds of thousands of students. Known for a relentless eye for quality and creativity-enhancing interfaces, he favors high-level abstractions that empower creators across disciplines.
18 years of coding experience
11 years of employment as a software developer
Math Science Computers Education Music Philosophy English, Math Science Computers Education Music Philosophy English at School of Hard KnocksBA Mathematics, BA Mathematics at University of Colorado Boulder
Stackoverflow
1,744reputation
41kreached
14answers
13questions
Github Skills (15)
clojure10
neural-network10
machine-learning10
clojure-cli10
algorithms8
data-structures8
algorithm8
data-structure8
lilypond6
web-bluetooth6
mono6
csv6
println6
android6
bluetooth6
Programming languages (20)
Github contributions (5)
originrose/cortex
Oct 2016 - Dec 2017
Machine learning in Clojure
Role in this project:
ML Engineer
ML EngineerContributions:48 commits, 49 PRs, 52 pushes in 1 year 2 months
Contributions summary:Harold focused on implementing and improving machine learning functionalities within the Clojure-based project. Contributions include enhancements to the neural network training process, specifically related to learning attenuation. They also worked on improving error messages, adding tests for softmax channel loss, and addressing dataset-related issues. The user's work suggests a strong involvement in the core machine learning aspects of the project.
clojuremachine-learning
harold/dot-emacs
Feb 2016 - Jan 2023
Contributions:11 pushes in 7 years
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