Mustafa Mukadam
Research Lead at Amazon
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolMustafa Mukadam is a research lead and robotics/AI scientist with 11 years of experience advancing dexterous manipulation and touch perception at industry leaders including Meta and Amazon. He holds a PhD in Robotics from Georgia Tech and combines deep academic rigor with hands-on engineering, contributing to open-source projects like Facebook Research's Theseus (differentiable nonlinear optimization) where he maintained code and created practical examples. At Amazon he now leads dexterous manipulation efforts in the Industrial Robotics Group after directing learned manipulation teams for large-scale robotic stowing, and he also serves as an Affiliate Assistant Professor at the University of Washington. Mustafa’s profile reflects a rare blend of research leadership, production-focused applied science, and a demonstrated knack for making complex optimization tools accessible through clear examples.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD), Robotics, Doctor of Philosophy (PhD), Robotics at Georgia Institute of TechnologyMaster of Science (MS), Aerospace Engineering - Robotics, Master of Science (MS), Aerospace Engineering - Robotics at University of Illinois Urbana-ChampaignBachelor of Engineering (BEng), Mechanical Engineering, Bachelor of Engineering (BEng), Mechanical Engineering at University of Mumbai
English, Hindi, Marathi, Gujarati
Github Skills (2)
python10
pytorch10
Programming languages (3)
Github contributions (5)
facebookresearch/theseus
Dec 2021 - Sep 2022
A library for differentiable nonlinear optimization
Role in this project:
Backend Developer
Backend DeveloperContributions:10 releases, 1049 reviews, 12 commits in 10 months
Contributions summary:Mustafa primarily contributed to code maintenance and example creation within the Theseus library. Their work included adding missing license headers to various files, ensuring proper attribution. They also added a simple example, demonstrating usage of the library for curve fitting and optimization. Additionally, they updated the example and the `__init__.py` to reflect a version bump.
pytorchroboticsdifferentiablebilevel-optimizationimplicit-differentiation
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