Summary
Ninad Khargonkar is an applied scientist with 11 years of experience bridging ML research and robotics, currently working on stow solutions at Amazon Fulfillment Technologies & Robotics. He holds advanced degrees from UMass Amherst and is a PhD candidate at UT Dallas where he developed representation learning for object-centric grasps, 3D environment automation, and remote rehabilitation systems combining 3D pose estimation and inverse dynamics. His industry work spans robotics startups and research labs—developing generative grasp models at Covariant and 3D point-cloud skeletonization at Kitware that earned an IEEE ISBI oral presentation. Comfortable moving between ML theory (submodular selection, fast inference) and applied multi-modal robotics, he builds models that translate real-world sensor inputs into actionable robot behaviors. Based in Seattle, he pairs strong academic foundations with production-focused research, and his playful GitHub bio hints at a fondness for numerical curiosity and large combinatorics.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The University of Texas at Dallas
Indian Institute of Technology Kanpur
Master's degree Computer Science, Master's degree Computer Science at University of Massachusetts Amherst
High School Diploma, High School Diploma at DAV Public School, Nerul
English, Marathi, Hindi