Postdoctoral Researcher at University of California, Berkeley
Irvine, California, United States
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Summary
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Rockstar
Sai Karimireddy is a postdoctoral researcher at UC Berkeley with a decade of experience applying optimization and machine learning techniques to practical problems. Based in Irvine, California, he blends academic rigor with hands-on engineering, exemplified by implementing Frank-Wolfe algorithm exercises and low-rank matrix completion baselines for an EPFL course repository. His work spans objective design, gradient computation, constrained projections, and projected gradient descent—skills that translate directly to recommendation and large-scale ML systems. Sai’s profile reflects both research depth and practical code contributions, indicating an ability to move theory into reproducible implementations. Colleagues can expect a researcher who writes production-minded experimental code and digs into algorithmic details often glossed over in higher-level research.
EPFL Course - Optimization for Machine Learning - CS-439
Role in this project:
ML Engineer
Contributions:28 commits, 23 pushes in 2 years
Contributions summary:Sai implemented practical exercises related to the Frank-Wolfe algorithm in a low-rank matrix completion project. Their contributions involve code modifications to implement several baselines such as global mean, user mean and item mean predictions to solve the movie recommendation using low rank matrix completion problem. Code updates were made to the matrix completion problem where the user needs to compute objective function, the gradient, project onto simplex and trace norm ball, and learn a matrix using projected gradient descent.
SGD with compressed gradients and error-feedback: https://arxiv.org/abs/1901.09847
Contributions:4 commits, 1 PR, 1 push in 3 years 5 months
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Sai Karimireddy - Postdoctoral Researcher at University of California, Berkeley