I’m a cryptography engineer at Apple, working on privacy-enhancing technologies. So far, my primary focus has been on homomorphic encryption.

Prior to joining Apple, I was a research scientist at Intel. While at Intel, I led development for Intel HEXL, an open-source C++ library accelerating homomorphic encryption using AVX512. I’ve also worked on the Intel Homomorphic Encryption Toolkit. I also worked on nGraph-HE, an open-source framework for deep learning inference on homomorphically encrypted data.

I received my M.S. in Computational and Mathematical Engineering from Stanford University. I received my B.S. in Computer Science and B.S. in Applied and Computational Mathematics from Caltech.

Interests

I enjoy tackling problems at the intersection of mathematics and software engineering. I am currently working on homomorphic encryption and other privacy-preserving machine learning technologies.

I also enjoy learning the nitty gritty details of C++ (I recommend reading Scott Myers’ Effective Modern C++) and optimizing software development workflow using modern tools such as iTerm, VS Code, pre-commit, and clang-format.

My personal interests include running, cycling, soccer, and playing clarinet.