Akshay Agrawal

Master's Student Optimization Group, ISL Dept. of Computer Science Stanford University Github Resume Email: akshayka at cs (dot) stanford (dot) edu

Bio

I am a Master's student in theoretical computer science and artificial intelligence at Stanford University, where I work with Professor Stephen Boyd and am advised by Professor Mehran Sahami. My research interests include accelerating first-order methods for convex optimization and encoding optimization problems via domain-specific languages.

I graduated with a B.S. in computer science from Stanford in 2016, with a concentration in software systems, and a minor in mathematics. During my undergraduate career, I collaborated with Andreas Paepcke to compile a dataset of online educational discussion forum posts, labeled with pedagogical information; we published our dataset and a machine learning intervention pipeline in the 2015 International Educational Data Mining Conference. I also served as a writer, news desk editor, and investigative news editor at The Stanford Daily.

I've spent four summers as an intern at Google, sampling a different technical infrastructure team each year. In the fall of 2017, I will take a break from academia to join the TensorFlow team as a full-time employee.

Research

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

• Investigations into Accelerating Alternating Projections. Ongoing work. A. Agrawal. 2017

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

Writings

• Optimization+ML Paper Summaries. Sumamries 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.

Teaching

• EE 364a: Convex Optimization I. Teaching assistant. Spring 2016-17.

• CS 221: Artificial Intelligence, Principles and Techniques. Teaching assistant. Autumn 2016-16.

• CS 109: Probability for Computer Scientists. Teaching assistant. Winter 2015-16, Spring 2015-16, Winter 2016-17.

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