The primary goal of the internship was to research new approaches to computing low-rank matrix completions, such as the Hadamard Multifactorization, with a focus on applications like recommendation systems (e.g., movie/music recommendations) and for Natural Language Processing. I implemented high-performance solvers for computing approximate low-rank matrix factorizations using Weighted-Alternating-Least-Squares. The solvers were written in python using numpy and scipy, and continued to be used by the team for ongoing experiments after my internship ended. As part of the research, I demonstrated cases where Hadamard Multifactorization outperforms traditional low-rank matrix completion for computing word embeddings, particularly when computing embeddings for several languages simultaneously.

- Derived and developed fast iterative methods for (possibly non-symmetric) saddle-point linear systems; such linear systems are ubiquitous within engineering applications. - Work was performed in collaboration with Prof. Chen Greif.

- Developer for mobile and iOS Gmail, on client and server-side - Worked in Java and Javascript - Responsible for writing design documents, implementation and testing of projects - Intern project resulted in first network responses to return 75% faster than before

- Organized and taught tennis classes for children - Instructed children on technique, style of play and sportsmanship

- Tested various software projects, wrote bug reports - Demonstrated working products to clients - Wrote automated test scripts in Ruby