Bio

I am a first-year Ph.D. student in the Information Systems Laboratory at Stanford University, where I am advised by Professor Stephen Boyd. I conduct research in optimization and machine learning, along with their applications. I am one of the primary designers of and contributors to 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 eager execution.

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 Geometric Programming. A. Agrawal, S. Diamond, S. Boyd. Pre-print. December 2018. (code, tutorial)

• A Rewriting System for Convex Optimization Problems. A. Agrawal, R. Verschueren, S. Diamond, S. Boyd. Journal of Control and Decision. January 2018. (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. (request 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.

• 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.