Avinash Balachandran is the Manager of the Extreme Vehicle Dynamics Control (EVDC) team at Toyota Research Institute (TRI). He works on understanding how to control the vehicle in all kinds of challenging scenarios including traction limited events, adverse weather conditions and drifting. To do this, he works closely with TRI's research partners and production partners to bridge the gap between research and production. Prior to joining TRI, he was the manager of the motion planning team at Faraday Future where his team worked on developing highway assist and valet parking features. He was also one of the early engineers of the Uber self driving project and worked on the various controls and safety systems on the fleet.

Avinash earned a BSc in Mechanical Engineering at Cornell University. Following this he also earned his MS and PhD degrees in Mechanical Engineering at Stanford University where he worked in the Dynamic Design Lab focusing on the shared control between autonomous vehicles and humans. He is passionate about building better, safer cars that can save lives.

Toyota Research Institute Inc. (TRI) is an R&D enterprise with an initial focus on artificial intelligence and robotics. The headquarters is located in Silicon Valley near Stanford University in Los Altos, California. A second facility is located near the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. A third facility is located near the University of Michigan in Ann Arbor, Michigan. TRI has four initial mandates: First, we wish to enhance the safety of automobiles with the ultimate goal of creating a car that is incapable of causing a crash, regardless of the skill or condition of the driver. Second, we want to increase access to cars to those who otherwise cannot drive, including people with special needs and seniors. Third, we plan to help translate Toyota’s expertise in creating products for outdoor mobility into products for indoor mobility. In other words, Toyota’s goal is to move people across the room, across town, and across the country. Finally we hope to accelerate scientific discovery by applying techniques from artificial intelligence and machine learning particularly in the area of materials science.