Aman Jhunjhunwala is a Senior Applied Scientist with 11 years of experience building production-ready AI and computer vision systems, currently applying multimodal ML to video, image, and language problems for Microsoft Copilot and Office. He previously led research engineering at NVIDIA on Isaac/Drive, designing performant multi-sensor fusion and edge-to-cloud vision pipelines, and internships at ETH Zurich and Caltech sharpened his expertise in edge inference and evolutionary ML. A top-ranked Computer Science graduate with a Master’s from Waterloo and a gold-medal BTech, he combines strong theoretical grounding with hands-on systems engineering. He also contributes to open-source scientific tooling—improving Astropy tutorials’ UX and docs—reflecting a pragmatic focus on developer experience as well as research impact.
11 years of coding experience
6 years of employment as a software developer
High School, Indian School Certificate (ISC), 93%, High School, Indian School Certificate (ISC), 93% at St.Xavier's Collegiate School
Bachelor’s Degree, Computer Science and Engineering, CGPA : 9.30 , Rank 1 / Gold medal, Bachelor’s Degree, Computer Science and Engineering, CGPA : 9.30 , Rank 1 / Gold medal at Birla Institute of Technology, Mesra
Master of Mathematics, Computer Science, 9.6/10, Master of Mathematics, Computer Science, 9.6/10 at University of Waterloo
Contributions:37 commits, 4 PRs, 32 comments in 2 months
Contributions summary:Aman primarily focused on enhancing the user experience of the Astropy tutorials. Their contributions included adding features like Disqus comments and "Edit on Github" links to the tutorials. They also made adjustments to the HTML templates and CSS styling and implemented an environment check. Additionally, the user fixed indentation and code formatting issues.
Contributions:175 pushes, 1 branch in 5 years 5 months
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Aman Jhunjhunwala - Senior Applied Scientist at Microsoft