Shivesh Khaitan is a Senior Machine Learning Engineer based in San Francisco with eight years of experience building perception and decision systems for robotics and autonomous vehicles. He blends academic rigor from an MS in Robotics at Carnegie Mellon with hands-on production work at Aurora and InstaDeep, and published an ICLR 2024 paper on using latent diffusion models for long-horizon offline robotic planning. His background spans computer vision, reinforcement learning, sensor fusion and multi-robot planning, with internship experience at Google’s Everyday Robots and contributions to ROS2/Gazebo integrations. Comfortable moving research into real-world stacks, he has designed ML pipelines in JAX/Python and engineered optimization objectives for real-time motion planners, an uncommon mix that accelerates deployment of advanced autonomy.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Technology, Computer Science, Bachelor of Technology, Computer Science at Manipal Institute of Technology
Master of Science - MS, Robotics, Master of Science - MS, Robotics at Carnegie Mellon University
Driver for the Continental radar ARS_404 / ARS_408.
Contributions:38 commits, 10 pushes, 1 branch in 3 months
px4cockpitarsradar
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