Simon Niklaus
Staff Research Scientist at Google DeepMind
Vancouver, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSimon Niklaus is a Staff Research Scientist with 11 years of experience at the intersection of computer vision and machine learning, now working on GenAI data at Google DeepMind after leading foundational research and product launches at Adobe Firefly. He has a strong track record shipping production features—image denoising, lens blur, sky segmentation, optical flow, and creative mockup tools—and his open-source work includes PyTorch implementations of PWC-Net, separable-convolution SLOMO, and a 3D Ken Burns effect that highlight deep expertise in video interpolation and efficient model/kernel design. A PhD candidate in computer science, Simon combines rigorous academic training with hands-on CUDA and PyTorch engineering, often optimizing kernels and cost-volume layers for real-world performance. Notably, he was one of five founders of Adobe Firefly’s data efforts and continues to publish practical, well-engineered repos that bridge research prototypes and production systems.
12 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Portland State UniversityMaster of Science (M.Sc.) Information Systems, Master of Science (M.Sc.) Information Systems at Technical University of Applied Sciences Würzburg-Schweinfurt (THWS)
German, English
Github Skills (15)
cuda10
computer-vision10
pytorch10
deeplearning-ai10
deep-learning10
python10
neural-network9
machine-learning9
convolutional-neural-networks9
c-language8
faster-rcnn8
mask-rcnn8
video-processing8
image-processing8
cprogramming-language8
Programming languages (11)
Github contributions (5)
sniklaus/pytorch-pwc
Jul 2018 - Nov 2022
a reimplementation of PWC-Net in PyTorch that matches the official Caffe version
Role in this project:
ML Engineer
ML EngineerContributions:49 commits, 1 PR, 44 pushes in 4 years 4 months
Contributions summary:Simon appears to be focused on optimizing the PWC-Net implementation. Their commits primarily involve modifying the `run.py` file to improve image preprocessing and integration of the custom cost volume layer `correlation`. Further adjustments to the `correlation` library and the addition of a comparison tool demonstrate effort in fine-tuning the network's performance and functionality.
caffepytorchpythoncudadeep-learning
sniklaus/3d-ken-burns
Nov 2019 - Mar 2022
an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
Role in this project:ML Engineer
ML EngineerContributions:43 commits, 5 PRs, 44 pushes in 2 years 4 months
Contributions summary:Simon's contributions primarily involve modifications to the codebase related to 3D Ken Burns effect implementation using PyTorch. The commits demonstrate changes in various files including model definitions, kernel implementations, and benchmark scripts, suggesting the user is working on model architecture and performance optimizations. The user also modified the code to ensure that the project is compatible with pytorch version 1.2.0 and later.
3dpytorchpythoncudadeep-learning
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