Anusha Nagabandi
Applied Scientist at Amazon Fulfillment Technologies & Robotics
Berkeley, California, United States
Summary
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Senior🎓
Top SchoolAnusha Nagabandi is a Berkeley-based applied scientist known for translating research in machine learning and robotics into scalable systems. With 9 years of experience, she currently applies advanced ML and robotics techniques at Amazon Fulfillment Technologies & Robotics, building reliable training pipelines and policy models for real-world automation. Previously, she served as a Research Scientist at Covariant, where she worked on robotics and AI policy algorithms, and she contributed to Google AI as a research intern during her PhD studies. She earned a PhD in Electrical Engineering and Computer Science from UC Berkeley, where she focused on areas overlapping deep reinforcement learning and autonomy. Her open-source contributions include hands-on improvements to the berkeleydeeprlcourse homework pipeline, highlighting a dedication to practical ML education and reproducible research. Based in the Bay Area, she blends academic rigor with hands-on engineering to drive intelligent systems from research ideas to production.
9 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, BerkeleyUniversity of Illinois Urbana-Champaign
Github Skills (6)
machine-learning10
python10
reinforcement-learning10
tensorflow9
tensorflow29
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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