Rushil Goradia

Technical lead working on foundational models, trajectory optimization, collision avoidance and system architecture for self driving cars.

Intern on the Supply Chain Automation (SCA) team. Auditing current and developing product lines, and providing feedback on areas of improvement. Additionally, helping the hardware teams in designing and implementing a system to automate warehouse procedures. This includes determining system power specifications based on hardware requirements, and developing a test robot to collect data for warehouse mapping applications.

Intern in the Manufacturing-on-Demand team, part of Amazon Fulfillment Technologies (AFT). Designing and implementing a full stack tool for adding functionality to a service that manages the entire manufacture- on-demand process. Syncing this application with the overall Amazon manufacturing process and working with associated databases and upstream services to send and receive relavant data.

Working as part of the team developing the Berkeley Autonomous Race Car (BARC) project at the MPC Lab. The BARC is an RC car that has been retrofiited to test control algorithms for lane-keeping and autonomous parking. My current project involves integrating a LIDAR on the BARC, and figuring out how to use its data for localization, mapping and obstacle avoidance.

Worked as a calibration engineer for the Chevrolet Volt EREV at the GM Milford Proving Grounds. My work involved estimating the total power required by the car, especially power lost through spin losses in the transmission. I also developed transmission warm up curves for varying initial temperatures. Throughout my internship I studied controllers, interfaces and software used in the car. My final project also developed the electric propulsion and auxilliary power equations, and contributed to it’s propulsion feedback model.

Built both the client-side and server-side of a web application for a server management console using Javascript and AngularJS

M.S. Electrical Engineering program at Stanford focusing on Controls, Optimization and Learning for Robotics Systems.