Akshay Agrawal

A picture of Akshay.

Ph.D. Candidate in Electrical Engineering, Stanford University
M.S., B.S. in Computer Science, Stanford University
[email protected]

Github / Google Scholar / Resume / Blog


I am a first-year Ph.D. student at Stanford University, where I am advised by Professor Stephen Boyd; I am supported by a Stanford Graduate Fellowship. I conduct research in optimization and machine learning, along with their applications. I am one of the primary designers of CVXPY 1.0, a domain-specific language for convex optimization that is used by many universities and corporations. From 2017-2018, I was a full-time software engineer on the Google Brain team, where I worked on the core TensorFlow runtime and TensorFlow Eager, which is a key feature in TensorFlow 2.0

I graduated from Stanford in 2017 with a Master's in theoretical computer science and artificial intelligence, a B.S. in computer science with a concentration in systems, and a minor in mathematics. During my time as an undergraduate and Master's student, I published research in convex optimization with Professor Boyd and in applied machine learning with Dr. Andreas Paepcke. Throughout my time at Stanford, I have been fortunate to have Professor Mehran Sahami as a mentor.

If computer science is my first passion, then writing is my second: I served as a writer, news desk editor, and investigative news editor for The Stanford Daily, and I blog at debugmind.com.


Technical Reports


I spent six quarters as a teaching assistant for the following Stanford courses:


From 2017-2018, I worked on TensorFlow as an engineer on Google Brain team. Specifically, I developed a multi-stage programming model that lets users enjoy eager (imperative) execution while providing them the option to optimize blocks of TensorFlow operations via just-in-time compilation.

I honed my technical infrastructure skills over the course of four summer internships at Google, where I: