Anusha Nagabandi
Applied Scientist at Amazon Fulfillment Technologies & Robotics
Berkeley, California, United States
Summary
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Senior🎓
Top SchoolAnusha Nagabandi is an applied scientist based in Berkeley with ten years of experience bridging academic research and production robotics, currently building ML-driven solutions at Amazon Fulfillment Technologies & Robotics after four years as a research scientist at Covariant. She holds a Ph.D. in EECS from UC Berkeley and has contributed to deep reinforcement learning coursework and codebases, reflecting hands-on expertise tuning training pipelines and implementing policy models. Her background includes internships and research roles at Google AI, Carnegie Mellon, and MIT Lincoln Laboratory, giving her a strong foundation in control systems, perception, and real-world robotic systems. Known for translating cutting-edge research into scalable products, she combines rigorous experimental practice with practical engineering—an approach evident in her contributions to the berkeleydeeprlcourse repository optimizing trainers and MPC policies.
10 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science, Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science at University of California, BerkeleyBachelor’s Degree Electrical Engineering, Bachelor’s Degree Electrical Engineering at University of Illinois Urbana-Champaign
Github Skills (5)
machine-learning10
python10
reinforcement-learning10
tensorflow9
pytorch9
Programming languages (1)
Github contributions (5)
berkeleydeeprlcourse/homework_fall2019
Sep 2019 - Oct 2019
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 1 PR, 12 pushes in 1 month
Contributions summary:Anusha primarily made modifications related to machine learning model training and infrastructure. They removed unnecessary imports, updated trainer scripts, and adjusted parameters related to training batch sizes, and added missing imports. These changes suggest a focus on optimizing the training process and ensuring code correctness within the machine learning framework. Modifications to policy models and MPC policies further indicate involvement in algorithm implementation and refinement.
anagabandi/nn_dynamics
Dec 2017 - Jan 2018
Contributions:4 commits, 8 pushes, 3 branches in 1 month
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