Max Argus
Research Scientist at Ai2
Greater Seattle Area United States
Summary
👤
Senior🎓
Top SchoolMax Argus is a research scientist specializing in machine learning and robotics, with 14 years of experience spanning academic postdocs and industry research at institutions like the University of Freiburg and Ai2. He builds few-shot robot imitation and generalizable robotics algorithms, blending reinforcement learning, visual servoing, self-supervision, and hand-pose estimation into practical systems. His PhD and postdoc work under Thomas Brox and collaborators produced 20+ papers with strong citation impact, and he has published on explainable AI as well. An active open-source contributor, he has improved core tooling in widely used projects such as Caffe and the Bullet Physics SDK, tackling build-system robustness and real-time rendering integrations. Comfortable supervising students and securing collaborations across hospitals and labs, he pairs deep physics training with hands-on engineering that bridges research and deployable robotics.
13 years of coding experience
5 years of employment as a software developer
Master's degree, Physics, Master's degree, Physics at Heidelberg UniversityBachelor's degree, Physics, Bachelor's degree, Physics at Durham UniversityMunich International School
Github Skills (22)
simulation10
caffe10
c-language10
python10
simulations10
machine-learning10
cmake10
numpy10
deep-learning10
egl10
cprogramming-language10
opengl10
protobuffer10
protobuf10
game-development9
Programming languages (6)
Github contributions (5)
BVLC/caffe
Oct 2014 - Mar 2017
Caffe: a fast open framework for deep learning.
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 21 PRs, 97 comments in 2 years 6 months
Contributions summary:Max made multiple contributions to improve the build process, specifically related to the CMake build configuration for the pycaffe module and the integration of external dependencies like Protobuf and NumPy. They addressed issues with shared library naming, linking, and dependency finding. The user also added an ignore_label parameter to the AccuracyLayer, and made related fixes to crop layer and hdf5 utility functions.
pytorchvisiondeep-learningmachine-learningcaffe
bulletphysics/bullet3
May 2018 - Mar 2020
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 reviews, 25 commits, 14 PRs in 1 year 10 months
Contributions summary:Max primarily worked on integrating and improving an EGL plugin for the Bullet Physics SDK, enabling real-time collision detection and multi-physics simulation. They updated the EGL implementation, added features like camera image capture, and refactored rendering code. Additionally, the user refactored and streamlined the rendering test scripts and adjusted build configurations to accommodate the EGL integration.
pybulletbullet-physicssimulationsimulatorreal-time
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.