Nicolas Nytko
Graduate Research Assistant
Champaign, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolNicolas Nytko is a computationally-minded software engineer and PhD candidate in computer science with 12 years of experience bridging numerical methods, machine learning, and full-stack engineering. Currently a graduate research assistant at Los Alamos National Laboratory, he researches numerical methods for PDEs and brings practical software craftsmanship from prior internships at Epic, Groupon, and Enable Medicine. He contributes to open-source education tooling—improving PrairieLearn’s interactive inputs and visualization components—demonstrating an eye for cross-browser front-end quirks and backend integration. Nicolas pairs rigorous academic training (BS/MS with high honors from UIUC) with hands-on product experience, able to move between developing research-grade algorithms and shipping user-facing features. A less obvious strength is his knack for turning subtle UI bugs into robust component enhancements, improving reliability for diverse users and browsers.
12 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Illinois Urbana-ChampaignTransfer, Computer Science, 3.60/4.00, Transfer, Computer Science, 3.60/4.00 at Harper College
Github Skills (5)
python10
autograd10
javascript6
html5
testing4
Programming languages (10)
Github contributions (5)
PrairieLearn/PrairieLearn
Oct 2018 - Nov 2022
Online problem-driven learning system
Role in this project:
Back-end Developer
Back-end DeveloperContributions:100 reviews, 160 commits, 185 PRs in 4 years 1 month
Contributions summary:Nicolas primarily focused on fixing bugs related to rendering matrix input on Safari browsers and adding new features to the `pl-number-input` element, such as disabling help text and adding a size attribute. They also contributed to the development of new elements like `pl-python-variable` for displaying Python variables and `pl-graph` for rendering graphs. Their work involved modifications to Python files, including updating and improving the existing code structure.
machine-learningproblemassessmentdrivingautograders
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