Jordan Matelsky
Computational Neuroscientist
Philadelphia, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolJordan Matelsky is a computational neuroscientist with 12 years of experience applying machine learning and neuroengineering to real-world problems, currently leading research at Johns Hopkins APL while pursuing a PhD in Bioengineering at the University of Pennsylvania. He bridges neuroscience and software: contributing backend mathematical functionality to the popular TensorLy library for multiple frameworks and improving front-end UX for a speed-reading Chrome extension. Comfortable across stacks, he has a track record of shipping robust scientific code, prototype medical devices through his startup experience, and translating complex brain data into practical algorithms. Based in Philadelphia, he combines academic rigor from Johns Hopkins and Penn with entrepreneurial instincts and a pragmatic focus on interoperability across NumPy, PyTorch, JAX and TensorFlow. An under-the-radar strength is his ability to harmonize low-level numerical consistency with user-facing design, making research tools both correct and usable.
12 years of coding experience
Johns Hopkins UniversityRoxbury High SchoolPhD, Bioengineering, PhD, Bioengineering at University of Pennsylvania
Stackoverflow
2,341reputation
205kreached
63answers
23questions
autocomplete
top-5%
google-colaboratory
top-5%
Github Skills (32)
pytorch10
javascript10
python10
algebra10
decomposition10
tensorrt10
css10
numpy10
tensorflow10
front-end-development10
html10
tensor10
logger9
browser-extension9
user-interface9
Programming languages (25)
Github contributions (5)
tensorly/tensorly
Mar 2022 - Jul 2022
TensorLy: Tensor Learning in Python.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 19 commits, 3 PRs in 3 months
Contributions summary:Jordan primarily contributed to the `tensorly` library by adding and refining mathematical functions within the backend implementations. Their work focused on expanding the available functions, including trigonometric functions and mathematical constants, across various backend frameworks such as NumPy, JAX, PyTorch, TensorFlow, MXNet, and CuPy. These changes involved modifying the core backend functionalities, ensuring consistency and compatibility across different tensor processing environments. The user's efforts improved the library's feature set and maintained cross-platform compatibility.
tensor-factorizationtensor-decompositionpythontensor-learningtensor
ds300/jetzt
Mar 2014 - Mar 2014
Speed reader extension for chrome
Role in this project:Front-end Developer
Front-end DeveloperContributions:13 commits, 1 comment in 3 days
Contributions summary:Jordan primarily contributed to the front-end development of the "jetzt" speed reader extension. They implemented UI changes, including the addition of a new options visualizer using Angular and enhanced the existing options UI, such as allowing custom colors and fonts. Their work involved modifying HTML, CSS, and JavaScript files to integrate new features and improve the user interface, with a focus on visual customization and theming. The user also worked on integrating a real-time preview to the options menu.
speedspeed-readerchromereaderchrome-extension
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