Bio

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.

Publications

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

• Differentiating Through a Conic Program. A. Agrawal, S. Barratt, S. Boyd, E. Busseti, W. Moursi. Pre-print. 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] [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]

Technical Reports

• 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]

Teaching

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

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

• 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.

Industry

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.

Writings

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

• A Primer on TensorFlow 2.0. April 2019.

• Graphical Model Sketch. Lecture notes on a sketch for graphical models. June 2016.

• Max Stability and Misra Gries. Lecture notes on max stability and the Misra Gries algorithm. April 2016.

• Learning about Learning: Machine Learning and MOOCs. A blog post about one of my applied machine learning projects. June 2015.

• Machines that Learn: Making Distributed Storage Smarter. A blog post describing an initiative to make storage systems smarter via machine learning. Sept. 2014.