Russ Tedrake
Founder at Stealth Startup
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolRuss Tedrake is a founder and professor at MIT with 12+ years of experience at the intersection of physical AI, robotics, machine learning, optimization, and control. He led robotics research at Toyota Research Institute and now runs a stealth startup from Cambridge, bringing deep industry and academic perspectives to productizing advanced control and behavior models. A core contributor and software architect on the widely used Drake robotics library, he has implemented critical trajectory optimization and floating-base system features that bridge theory to robust code. Trained with a PhD in Computer Science from MIT and dual degrees in Computer Engineering from the University of Michigan, he combines rigorous research with hands-on systems engineering. Notably, his work advances new control paradigms—such as finite-horizon LQR—applied at scale in both research and industrial robotics.
12 years of coding experience
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Massachusetts Institute of TechnologyBachelors and Masters Computer Engineering, Bachelors and Masters Computer Engineering at University of Michigan College of Engineering
Stackoverflow
5,178reputation
32kreached
280answers
6questions
Github Skills (23)
c-language10
architecture10
trajectory-optimization10
cprogramming-language10
architectures10
optimization10
robotics9
mathematical9
linear-algebra9
modeling9
numerical-integration8
estimate7
state-machine7
state-space7
parameter-estimation7
Programming languages (13)
Github contributions (5)
RobotLocomotion/drake
Jan 2014 - Dec 2022
Model-based design and verification for robotics.
Role in this project:
Back-end Developer & Software Architect
Back-end Developer & Software ArchitectContributions:1 release, 1876 reviews, 3869 commits in 9 years
Contributions summary:Russ made significant contributions to the Drake robotics library, focusing on extending the capabilities of the systems and trajectories tools. Their work included implementing support for continuous and discrete-time systems, and adding a variety of features and fixes related to trajectory optimization, integration, and model representation (specifically with regard to the handling of floating-base bodies). Additionally, the user demonstrated their understanding of complex systems with new control paradigms such as finite-horizon LQR to support the core robotics features.
model-basedmodel-based-designroboticsdrakeverification
RobotLocomotion/gcs-science-robotics
Oct 2023 - Mar 2025
Motion Planning around Obstacles with Convex Optimization by Marcucci et al, 2023
Contributions:2 releases, 7 reviews, 9 PRs in 1 year 5 months
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