Carl Folkestad
Staff Machine Learning Engineer, Autonomy at Rivian
Los Angeles Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolCarl Folkestad is a Staff Machine Learning Engineer specializing in autonomy with 9 years of experience applying deep learning, reinforcement learning, and control theory to real-world robotic and vehicle systems. He holds a PhD in Control and Dynamical Systems from Caltech where he developed novel Koopman-based learning and safety-critical control methods that led to multiple ICRA/ACC publications. At Cruise he advanced behavior and trajectory selection models for real-time planning, and he now builds end-to-end driving models at Rivian, bridging research-grade theory with production autonomy stacks. Known for combining physics-informed approaches with data-driven learning, he has a track record of improving computational efficiency and safety in learned dynamics and control. Based in Los Angeles, he brings both academic rigor and hands-on systems engineering to large-scale autonomy challenges.
9 years of coding experience
8 years of employment as a software developer
Mechanical Engineering, Mechanical Engineering at Massachusetts Institute of TechnologyCalifornia Institute of TechnologyNorwegian University of Science and Technology
English
Github Skills (8)
nonlinear10
state-space10
decomposition10
dynamical-systems9
pytorch7
machine-learning7
deep-learning7
swarm3
Programming languages (3)
Github contributions (5)
Cafolkes/koopman-cbf
Aug 2020 - Nov 2020
Contributions:56 commits, 2 PRs, 9 pushes in 3 months
Cafolkes/keedmd
Sep 2019 - Apr 2020
Repository for construction of Koopman eigenfunctions for unknown dynamical systems and identification of a lifted state-space model using Koopman Eigenfunction Extended Dynamic Mode Decomposition (KEEDMD).
Contributions:176 commits, 15 PRs, 18 pushes in 7 months
decompositionextendeddynamical-systemsunknownkoopman
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