# Patrick Washington > Robotics | Autonomous Vehicles | Stanford Aero/Astro PhD Location: San Francisco Bay Area, United States Profile: https://flows.cv/patrickwashington I recently started as a motion planning software engineer at Zoox, working on the TeleGuidance team. Before that, I was a research/software engineer at Gatik, working on autonomous trucking. At Gatik, I owned features in perception (obstacle tracking) and planning, which are currently deployed on trucks driving on public roads. I earned a PhD in the Multi-Robot Systems Lab in the Stanford University Department of Aeronautics and Astronautics. My focuses in school were dynamics and control of robotic systems, path planning, and reinforcement learning. ## Work Experience ### Software Engineer, TeleGuidance @ Zoox Jan 2026 – Present | San Francisco Bay Area ### Senior Research Engineer @ Gatik Jan 2025 – Jan 2026 | Mountain View, CA ### Research Engineer II @ Gatik Jan 2024 – Jan 2025 | Mountain View, California, United States I write real-time, testable C++ code for perception and planning features that are running on trucks on the road right now. Perception: - Wrote and integrated nonlinear dynamics models and filters to estimate the states of obstacles such as other vehicles and pedestrians. This improved tracking, reducing false motion and harsh brakes from false positive detections without increasing false negatives. - Worked with the safety team to address test cases to improve obstacle detection. Planning: - Developed a feature to ingest noisy map data and estimate road grade and bank angles. This significantly improved the truck's performance when starting/stopping on hills, along with general improvements to normal driving. - Developed a novel anytime search algorithm for the global planner. This enables nonconvex decision making while improving compute time. - Integrated performance metrics to ensure features stayed within compute budgets, along with determining what parts of the planner needed optimizing. Miscellaneous: - Triaged issues reported by the test engineers. This involved visualizing data to determine whether the truck's behavior was expected and determine the cause if unexpected. - Wrote a map visualization tool to assist in debugging behaviors and finding mapping errors. - Helped onboard new employees. ### Graduate Student (Masters & PhD) @ Stanford University School of Engineering Jan 2017 – Jan 2024 Thesis: Efficient and Reconfigurable Approximate Value Functions for Task Scheduling, Path Planning, and Control. Multi-Robot Systems Lab Out of Distribution Detection for Image-Based Systems Modeled in the Latent Space - Developed a method for OOD detection to use when planning in the latent space of a learned model using an autoencoder self-consistency metric. - Works alongside previous work on graph-based methods. Path Planning and Control using Learned Dynamic System Models - Worked on tree- and graph-based algorithms to build value functions for and to control systems whose dynamics are treated as a black box. This allows the methods to be applied to learned models of the systems without needing physics equations. Task Scheduling - This work focused on the persistent surveillance problem, which requires drones to monitor a region while intelligently charging, enabling long-term coverage. The problem has direct applications to autonomous taxis and warehouse management. - Developed an algorithm that reduced the problem to a tractable system to achieve near-optimal performance, giving greater than 10x time improvement over optimal methods while significantly outperforming heuristic-based methods. ### Course Assistant (TA) @ Stanford University School of Engineering Jan 2024 – Jan 2024 AA 212 Advanced Feedback Control Design ### Course Assistant (TA) @ Stanford University School of Engineering Jan 2022 – Jan 2022 AA 212 Advanced Feedback Control Design ### Course Assistant (TA) @ Stanford University School of Engineering Jan 2021 – Jan 2021 AA212 Advanced Feedback Control Design ### Course Assistant (TA) @ Stanford University School of Engineering Jan 2021 – Jan 2021 AA273 State Estimation and Filtering for Aerospace Systems ### GNC Intern @ Reliable Robotics Corporation Jan 2022 – Jan 2022 | Mountain View, California, United States I developed a plan for handling the engine-out failure case for an autonomous plane. ### Undergraduate Research Assistant @ University of Maryland - A. James Clark School of Engineering Jan 2016 – Jan 2017 Composites Research Lab Hydraulic Artificial Muscles - Built a starfish-like arm that used artificial muscles driven by water and designed a controller that was able to position the end-effector despite highly nonlinear dynamics. - Part of a multi-university effort to design a robot to perform underwater tasks. - Repurposed an espresso machine's pump to drive the muscles, providing further evidence of the value of coffee in research. ### Undergraduate Research Assistant @ University of Maryland - A. James Clark School of Engineering Jan 2014 – Jan 2017 | University of Maryland Collective Dynamics and Control Laboratory Flexible Fish Robot - Designed and assembled the hardware for a flexible fish-like robot driven by a reaction wheel. - Used 3D modeling for the robot and built test apparatus. Flow Sensing - Created apparatus to generate vortices in water. - Used computer vision to track the flow. ### Teaching Fellow (Undergraduate TA) @ University of Maryland - A. James Clark School of Engineering Jan 2017 – Jan 2017 ENAE432 Control of Aerospace Systems ### High School Intern @ University of Maryland - A. James Clark School of Engineering Jan 2012 – Jan 2012 Collective Dynamics and Control Laboratory ## Education ### Doctor of Philosophy - PhD in Aerospace, Aeronautical and Astronautical Engineering Stanford University ### Master of Science - MS in Aerospace, Aeronautical and Astronautical Engineering Stanford University ### Bachelor of Science - BS in Aerospace Engineering University of Maryland ### Montgomery Blair High School Science, Math, and Computer Science Magnet ## Contact & Social - LinkedIn: https://linkedin.com/in/patrick-washington --- Source: https://flows.cv/patrickwashington JSON Resume: https://flows.cv/patrickwashington/resume.json Last updated: 2026-04-11