Bio

I am a second-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 principal developers of CVXPY 1.0, a domain-specific language for convex optimization that is used by many universities and companies. Previously, I was a full-time software engineer on the Google Brain team, where I worked on TensorFlow 2.0 and the core TensorFlow runtime.

I graduated from Stanford in 2017 with a B.S. and M.S. in computer science and a minor in mathematics. I focused my studies in computer systems, artificial intelligence, and theory. During my undergraduate and Master's career, I published research in convex optimization and applied machine learning with Professor Boyd and 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.

Publications

• Disciplined Quasiconvex Programming. A. Agrawal and S. Boyd. Pre-print. May 2019. [bibtex] [code]

• Differentiating through a Cone Program. A. Agrawal, S. Barratt, S. Boyd, E. Busseti, W. Moursi. Journal of Applied and Numerical Optimization. April 2019. [bibtex] [code]

• TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning. A. Agrawal, A. N. Modi, A. Passos, A. Lavoie, A. Agarwal, A. Shankar, I. Ganichev, J. Levenberg, M. Hong, R. Monga, S. Cai. Systems for Machine Learning (SysML) 2019. March 2019. [bibtex] [slides] [blog post] [code]

• Disciplined Geometric Programming. A. Agrawal, S. Diamond, S. Boyd. Optimization Letters. March 2019. [bibtex] [tutorial] [poster] [code]

• A Rewriting System for Convex Optimization Problems. A. Agrawal, R. Verschueren, S. Diamond, S. Boyd. Journal of Control and Decision. January 2018. [bibtex] [code]

• YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips. A. Agrawal, J. Venkatraman, S. Leonard, and A. Paepcke. Educational Data Mining. 2015. [bibtex] [dataset] [code]

Industry

I am available for consulting related to CVXPY and TensorFlow, as well as mathematical programming and machine learning more broadly. Contact me to schedule an initial consultation.

I have industry experience in designing and building software for machine learning (TensorFlow 2.0), motion planning and control for autonomous vehicles, and performance analysis of Google-scale software systems.

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:

• applied data science to conduct fleet-wide performance analyses of programs in shared servers and datacenters;

• analyzed Dapper traces for the distributed storage stack and uncovered major performance bugs;

• built a simulator for solid-state drives and investigated garbage reduction policies;

• wrote test suites and tools for the Linux production kernel.

Teaching

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

• EE 364a: Convex Optimization I. Professor Stephen Boyd. Spring 2016-17, Summer 2018-2019.

• CS 221: Artificial Intelligence, Principles and Techniques. Professor Percy Liang. Autumn 2016-17.

• CS 109: Probability for Computer Scientists. Professor Mehran Sahami and Lecturer Chris Piech. Winter 2015-16, Spring 2015-16, Winter 2016-17.

• CS 106A: Programming Methodology. Section Leader. Lecturer Keith Schwarz. Winter 2013-14.

Writings

• A Primer on TensorFlow 2.0. April 2019.

• Learning about Learning: Machine Learning and MOOCs. June 2015.

• Machines that Learn: Making Distributed Storage Smarter. Sept. 2014.

• Optimization+ML Paper Summaries. Summaries and commentary on papers that I find interesting. Feb. 2017-Present.

Technical Reports

• Separation Theorems. Lecture notes on separation theorems in convex analysis. A. Agrawal. 2019.

• A Cutting-Plane, Alternating Projections Algorithm for Conic Optimization Problems. A. Agrawal. 2017. [code]

• Cosine Siamese Models for Stance Detection. A. Agrawal, D. Chin, K. Chen. 2017. [code]

• Xavier : A Reinforcement-Learning Approach to TCP Congestion Control. A. Agrawal. 2016. [code]

• B-CRAM: A Byzantine-Fault-Tolerant Challenge-Response Authentication Mechanism. A. Agrawal, R. Gasparyan, J. Shin. 2015. [code]