Eric Heiden is a Senior Research Scientist at NVIDIA with a decade of experience building high-performance simulation and robotics systems. Based in Mountain View, he develops GPU-accelerated simulation technology such as Newton and contributes to the widely used nvidia/warp framework, focusing on performance profiling, numerical robustness, and cross-platform reliability. His background spans deep learning and robotics research roles at NVIDIA and Google, plus hands-on autonomy work at JPL and systems work at ISI and Two Sigma. He holds an MS and is pursuing a PhD in Computer Science from USC, blending rigorous academic training with production-grade engineering. Notably, he has implemented Ceres-based system identification for real double-pendulum data and experimented with neural control and friction models, reflecting a rare mix of physics-based modeling and ML-driven control. He combines low-level CUDA/C++ expertise with practical profiling and tooling to deliver faster, more accurate simulation stacks for robotics research and applications.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Southern California
Bachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at University of Rostock
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
Role in this project:
Back-end Developer
Contributions:6 reviews, 50 commits, 69 comments in 7 months
Contributions summary:Eric contributed to the `nvidia/warp` repository, a framework for GPU simulation and graphics, by implementing features related to performance and functionality. They added support for NVTX profiling in the `ScopedTimer` utility, enhancing the framework's ability to track and analyze performance bottlenecks. Code changes include modifications to articulation and build scripts, and the integration of finite number checks, indicating a focus on numerical accuracy and optimization. The user also addressed specific issues, such as correcting the loading of dynamic link libraries on Windows.
Docker image with OpenAI Gym, Baselines, MuJoCo and Roboschool, utilizing TensorFlow and JupyterLab.
Contributions:16 commits, 4 PRs, 31 pushes in 1 year 7 months
docker-imagejupyterlabmujocoopenai-gymtensorflow
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