Papers — Akshay Agrawal

I have a PhD from Stanford University, where I was advised by Stephen Boyd and worked on machine learning and optimization. I also have a BS and MS in computer science, both from Stanford.
* denotes alphabetical ordering of authors
Computing tighter bounds on the n-Queen's Constant via Newton's Method. [bibtex][code]
P. Nobel, A. Agrawal, and S. Boyd. Optimization Letters.
Allocation of fungible resources via a fast, scalable price discovery method. [bibtex][code]
A. Agrawal, S. Boyd, D. Narayanan, F. Kazhamiaka, M. Zaharia. Mathematical Programming Computation.
Embedded code generation with CVXPY. [bibtex][code]
M. Schaller, G. Banjac, S. Diamond, A. Agrawal, B. Stellato, S. Boyd. IEEE Control Systems Letters.
Minimum-distortion embedding. [bibtex][slides][code]
A. Agrawal, A. Ali, and S. Boyd. Foundations and Trends in Machine Learning.
Constant function market makers: Multi-asset trades via convex optimization. [bibtex]
G. Angeris, A. Agrawal, A. Evans, T. Chitra, and S. Boyd. Pre-print.
Learning convex optimization models. [bibtex][code]
A. Agrawal, S. Barratt, and S. Boyd.* IEEE/CAA Journal of Automatica Sinica.
Differentiating through log-log convex programs. [bibtex][poster][code]
A. Agrawal and S. Boyd. Pre-print.
Learning convex optimization control policies. [bibtex][code]
A. Agrawal, S. Barratt, S. Boyd, B. Stellato.* Learning for Dynamics and Control (L4DC), oral presentation.
Disciplined quasiconvex programming. [bibtex][code]
A. Agrawal and S. Boyd. Optimization Letters.
Differentiable convex optimization layers. [bibtex][code][blog post]
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter.* In Advances in Neural Information Processing Systems (NeurIPS).
Presented at the TensorFlow Developer Summit 2020, Sunnyvale [slides][video]
Differentiating through a cone program. [bibtex][code]
A. Agrawal, S. Barratt, S. Boyd, E. Busseti, W. Moursi.* Journal of Applied and Numerical Optimization.
TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning. [bibtex][slides][blog post][code]
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).
Disciplined geometric programming. [bibtex][tutorial][poster][code]
A. Agrawal, S. Diamond, S. Boyd. Optimization Letters.
Presented at ICCOPT 2019, Berlin [slides]
A rewriting system for convex optimization problems. [bibtex][slides][code]
A. Agrawal, R. Verschueren, S. Diamond, S. Boyd. Journal of Control and Decision.
YouEDU: Addressing confusion in MOOC discussion forums by recommending instructional video clips. [bibtex][dataset][code]
A. Agrawal, J. Venkatraman, S. Leonard, and A. Paepcke. Educational Data Mining.
Presented at EDM 2015, Madrid [slides]
I spent seven quarters as a teaching assistant for the following Stanford courses:
© 2026 Akshay Agrawal