Indian-origin Ashok Elluswamy became the first person to be hired for Elon Musk’s Tesla via Twitter. Elluswamy was hired in the autopilot team at his electric vehicle company eight years back, but now his name is again doing rounds on social. Thanks to a clip of an old interview of Musk where that has now gone viral.
"Ashok was the first person recruited from my tweet saying that Tesla is starting an Autopilot team!" Mr Musk said in a recent tweet in reply to the video on his interview, where he was heard taking Elluswamy’s name.
Elluswamy’s name has now piqued netizens' interest while they remain curious to know all about him.
Ashok is the head of Autopilot engineering at Tesla.
A couple of years back, Musk had taken to Twitter to ask people to apply for the role."I tweeted that Tesla is about to launch an autopilot team. Through that tweet, Ashok was the first to be selected in the autopilot team," Musk said in a tweet.
The application process was simple where interested candidates were asked to fill fields like name, email, exceptional work done in software, hardware or artificial intelligence and drop their resume in the PDF format.
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Who is Ashok Elluswamy?
Elluswamy joined Tesla in 2014 as a software engineer in the Autopilot department and over the year he rose to the ranks and now serves as the Director of the Autopilot Engineering team. Prior to Tesla, he worked with WABCO Vehicle Control System and interned with Volkswagon Electronic Research Lab.
Elluswamy identifies himself as a “robotics engineer with broad experience from computer vision and perception through planning and control.”
He holds a bachelor’s degree in Electronics and Communication Engineering from the College of Engineering Guindy, Chennai and a Master's degree in Robotics System Development from Carnegie Mellon University.
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His current role revolves around the creation of large scale automatic ground truth pipelines to train neural networks with massive amounts of diverse, high-quality data; developing an accurate and detailed geometric and semantic understanding of the world using the best of both machine-learned and engineered models; building robust, causal, predictive models for other agents in both geometry and semantic state spaces; decision making, motion planning, and control modules using state-of-the-art AI techniques including methods for high-dimensional search, trajectory optimization, reinforcement learning, model-predictive control.
