Thomas Müller
Principal Research Scientist at NVIDIA
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolThomas Müller is a Zurich-based software engineer with 12 years of experience who blends GPU-accelerated machine learning research with low-level graphics, audio, and polished UI work. Listed as a principal research scientist at NVIDIA on GitHub, he ships practical ML tooling and examples — notably improving NVLabs' tiny-cuda-nn benchmarking and image-learning workflows — while also contributing to graphics tooling like the HDR viewer tev used by graphics practitioners. His open-source footprint spans C++/CUDA kernels, OpenGL/Metal GUI libraries and Python bindings, including hands-on improvements such as manual mipmapping, sub-region texture uploads, and enhanced file-dialog/scroll behavior. Comfortable across full-stack domains, he routinely turns research prototypes into usable, well-tested developer-facing tools and intuitive UIs, from rhythm-game mechanics to image learning demos.
12 years of coding experience
7 years of employment as a software developer
ETH Zürich
Github Skills (45)
rythm10
bitmap10
cpp1710
ui-d10
c-language10
python10
image-processing10
cpp-1110
machine-learning10
ui-design10
file-dialog10
c1710
front-end-development10
c-plus-plus10
dotnet-core10
Programming languages (10)
Github contributions (5)
Tom94/tev
Aug 2017 - Jan 2023
High dynamic range (HDR) image viewer for graphics people
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:30 releases, 3 reviews, 927 commits in 5 years 5 months
Contributions summary:Thomas primarily worked on improving the tev image viewer application, contributing to the user interface by adding buttons and controls for manipulating the image, including the ability to toggle between LDR mode, copy and paste functionality and for managing layer and channel viewing. They also implemented functionality to improve the quality and control over display, in addition to resolving bugs in image loading and display to ensure smooth rendering and usability.
high-dynamic-rangescreenshothdrexrimage
NVlabs/tiny-cuda-nn
Apr 2021 - Jan 2023
Lightning fast C++/CUDA neural network framework
Role in this project:ML Engineer
ML EngineerContributions:5 releases, 13 reviews, 390 commits in 1 year 9 months
Contributions summary:Thomas made several changes to the benchmarking code, making it more usable and out-of-the-box compatible. They added functionality to save and load images for training, and improved the help message of the image learning script. The contributions focused on improving the image learning example for the tiny-cuda-nn framework.
cudapytorchmlpcppopencl
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