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Andrew Carr

I am a computational mathematician that specializes in machine learning. I was advised by David Wingate during my undergrad and master's programs, after which I joined OpenAI as a fellow. I spent some time as a research intern at Google Brain working with Quentin Berthet on applications of differentiable programming to self supervised learning on audio. I'm broadly interested in optimal transport, program synthesis, generative modeling, and theory of machine learning. Feel free to follow me on twitter for random musings about math / deep learning / programming but mostly memes.

Resume CV
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Cartwheel

I started something new!

take a look!

2023

Heretic

I am a senior technical advisor for Heretic venture studios

2023

Everyday Data Science: the course

I'm writing a new kind of interactive course. It's like a choose-your-own-adventure, except you'll learn Thompson sampling, differential equations, and Bayesian-optimal pricing!

Get the course on TigYog, or try the first chapter!

2022

Gretel AI

I'm starting as a senior applied research scientist at Gretel AI where I'm going to work on privacy preserving generative modeling.

2021+

OpenAI

I was a fellow on the code generation team at OpenAI where I helped build datasets for the Codex models which power GitHub copilot. I also did some internal research into more fundamental deep learning which was quite successful.

2021

Everyday Data Science: the book

I wrote my first book! It's a collection of cool applications of data science in everyday life.

Get a hard copy on Amazon or a half-priced PDF on gumroad

2021

Google Brain, Paris

I spent 6 months with the team working on applications of differentiable programming to unsupervised learning for audio, video, and images.

I had a blast

2020

Lyft, Level 5

I was a machine learning intern on the Prediction team in Lyft's automonous vehicles devision. I worked on dynamic vehicle motion prediction. I used a combination of physics based and data based predictive tools.

I also built a number of tools for my team that increased their development speed. These included a robust A/B platform, visualization tools, build scripts, and other process specific tools

2019

On the side

I have tons of random projects in applied math, machine learning, and data science. Feel free to check out my projects page or my blog.

2016-2020

Qualtrics

I worked as a machine learning engineering intern at Qualtrics on their text team. I had two successful projects during this time.

One was for spam detection which saved significant money in potential damages. The other project was a novel topic tracking algorithm. This work, which was a blend of LDA and probabilistic kalman filters, was eventually patented and is a key piece of technology for the team.

2018

Amazon Alexa Prize

I was a member of Team EVE for the Amazon alexa prize challenge. We were 1 of 8 teams selected out of 200+ for this competition.

We were tasked to build a social conversational agent for Alexa that could carry on a 20 minute conversation about arbitrary topics

This problem is still unsolved and research is ongoing.

I built offensive speech filtering tools that beat Amazon's internal tools. I also designed a complex sentiment analysis tool that operated as a heuristic for knowledge retrieval from the web

2018

Competitive Coding Instructor

I was approached by the director of the applied math program and asked if I could teach a class on competitive coding and algorithms. Mitch Probst and I designed the class, implemented curriculum, and taught for 2 semesters.

12 of our students placed in the top 20 of various competitions throughout the year. Many have since gone on to great programming jobs at top organizations.

2017-2018

Private Capital Group

I spent a summer working at a real estate investment firm as a software engineer. I built back office software for the law team that helped save hundreds of thousands of dollars per year. I also built a CRM for the sourcing group.

2016

Development TA

While a student in the applied math program. I worked to write programming labs for the curriculum. We were building the ship as we sailed. I worked on graph theory, optimization, profiling, computer vision, and optimal control theory.

I was also a TA for the control theory course while concurrently enrolled. This was another "trial by fire" experience in learning new material.

2016-2018

Perception, Control, Cognition Lab

I worked with Dr David Wingate for a number of years on fundamental machine learning research. I won a number of competitions, spoke to large groups, presented key findings, and published our work.

2016-2020

Domo

I worked as a quality assurance intern at Domo on their internal app store. I reviewed 100s of apps, and wrote several myself. These apps were used by customers to visualize different types of data from various sources.

2016

Carnegie Mellon University

I spent a summer in Pittsburgh as an IT Lab Fellow in the Heinz College. I researched body cameras and police violence along with videogames as a tool to assist refugees learning english.

2015

Math Research

Soon after discovering my love for mathematics, and switching into the applied and computational math program, I started working with Dr Vianey Villamizar on numerical solutions for the wave equation.

I was in over my head, but loved every moment. I took a few grad classes to get up to speed.

2015

BYU Studies

On campus I worked on a VB.NET application. I built a number of custom order websites in a variety of technologies. This work supported me through a large portion of my studies.

2014

Summit Engineering and Consulting

After high school, I worked as a web developer for an engineering firm. I was hired on with very minimal programming knowledge. I went to the local library and checked out every programming book they had. I'm not sure why I thought "Perl in a Nutshell" would help me style my HTML pages.

I made a few web sites for local businesses in the area and for the firm itself. It was an extremely formative time of self study.

2010