Ninad Khargonkar

Applied Scientist at Amazon Fulfillment Technologies & Robotics

Seattle, Washington, United States
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Summary

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Senior
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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.
code11 years of coding experience
job6 years of employment as a software developer
bookDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The University of Texas at Dallas
bookIndian Institute of Technology Kanpur
bookMaster's degree Computer Science, Master's degree Computer Science at University of Massachusetts Amherst
bookHigh School Diploma, High School Diploma at DAV Public School, Nerul
languagesEnglish, Marathi, Hindi
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Github Skills (19)

tensorflow9
object-detection8
robotics8
simulation7
benchmark7
prediction6
depth6
dataset-generation4
3d4
segmentation4
point-cloud4
pytorch3
machine-learning3
rss2
ros2

Programming languages (5)

CJavaScriptHTMLJupyter NotebookPython

Github contributions (5)

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kninad/chess-pgn-gif

Mar 2018 - Mar 2023

Contributions:14 pushes, 1 branch in 5 years
kninad/skeleton-nn

Jun 2022 - Aug 2022

DL based skeleton extraction on 2D/3D image and point cloud inputs.
Contributions:6 reviews, 76 commits, 11 PRs in 2 months
3dpoint-cloud
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