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Akshay Agrawal

A picture of Akshay.

Buildingmarimo.io
PhD in Electrical Engineering,Stanford University
MS, BS in Computer Science,Stanford University
akshayka@cs.stanford.edu

Github /Google Scholar /Twitter /LinkedIn /Blog

I'm currently buildingmarimo, a new kind ofreactive notebook for Python that's reproducible, git-friendly (stored asPython files), executable as a script, and deployable as an app.

I'm both a researcher, focusing on machine learningand optimization, and an engineer, having contributed to several open sourceprojects (including TensorFlow, when I worked at Google). I have a PhDfrom Stanford University, where Iwas advised byStephen Boyd (aswell as a BS and MS in computer science from Stanford).

Software

I build open source software designed to make machine learning and math actionable and accessible. Below are some of my projects.

marimo is a next-generation reactive Python notebook that's reproducible, git-friendly (stored as Python files), executable as a script, and deployable as an app.

PyMDE is a GPU-accelerated library for embedding large datasets and laying out graphs. PyMDE generalizes over 100 years' worth of embedding methods. Use it to embed single-cell transcriptomes, news documents, graphs, and more.
CVXPY is a parser-compiler for convex optimization that extends the reach of low-level numerical solvers. CVXPY is used by dozens of universities and companies, for problems in energy management, finance, resource allocation, and more, and has over half a million monthly downloads.

Papers

*denotes alphabetical ordering of authors

2022

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.Pre-print.

2021

Computing tighter bounds on the n-Queen's Constant via Newton's Method. [bibtex] [code]
P. Nobel, A. Agrawal, and S. Boyd.Pre-print
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.

2020

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.

2019

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]

2018

A rewriting system for convex optimization problems. [bibtex] [slides] [code]
A. Agrawal, R. Verschueren, S. Diamond, S. Boyd.Journal of Control and Decision.

2015

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 DataMining.
Presented at EDM 2015, Madrid [slides]

Industry

I enjoy speaking with people working on real problems. If you'dlike to chat,don't hesitate to reach out over email.

I have industry experience in designing and building software for machinelearning (TensorFlow 2.0), optimizing the scheduling of containers inshared datacenters, motion planning and control for autonomous vehicles,and performance analysis of Google-scale software systems.

From 2017-2018, I worked onTensorFlow asan engineer onGoogle Brain team.Specifically, I developed a multi-stage programming model that lets usersenjoy eager (imperative) execution while providing them the option to optimizeblocks of TensorFlow operations via just-in-time compilation.

I honed mytechnical infrastructure skills over the course of four summer internships atGoogle, where I:

Teaching

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

Essays

Technical Reports

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