Andrew Carr
Co-Founder And Chief Scientist at Utah Valley University
United States
Summary
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Rockstar🎓
Top SchoolAndrew Carr is a computational mathematician and entrepreneur with a decade of experience building and scaling applied AI products and research systems. As Co-Founder and Chief Scientist at Cartwheel and a founder/advisor at Arcade, he blends hands-on research (formerly at OpenAI and Gretel.ai) with product-driven engineering to ship models and tools that move audience-facing characters. His work on Codex at OpenAI and contributions to probml/pyprobml demonstrate a rare mix of deep theoretical knowledge—optimal transport, generative modeling—and practical visualization and reproducibility skills. Past roles at Google Brain, Lyft, and academic labs show a consistent focus on improving experimental throughput and performance (including a 2x HumanEval boost and major data-loading speedups). He also advises Utah Valley University's Applied AI Institute, signaling a commitment to mentoring the next wave of ML talent.
11 years of coding experience
7 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Brigham Young University
Tagalog, asl, French
Github Skills (13)
data-visualizations10
machine-learning10
data-visualization10
data-visualisation10
probabilistic-programming10
seaborn10
python10
numpy10
matplotlib10
linear-algebra9
svd9
tensorflow7
pytorch7
Programming languages (17)
Github contributions (5)
probml/pyprobml
Mar 2019 - May 2019
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 10 PRs, 6 comments in 1 month
Contributions summary:Andrew contributed significantly to generating visualizations and demonstrations related to probabilistic machine learning concepts. Their work included plotting the Bernoulli entropy function, demonstrating rotation and SVD operations, and creating figures for Newton's method, a coin model, and natural gradient descent, utilizing libraries like Matplotlib and Seaborn. These contributions aimed at illustrating core concepts within the "Probabilistic Machine Learning" book and project. Further, the user streamlined code by removing a matrix print function.
machine-learningpythonjupyter-notebookscolabpml
andrewnc/geometric-neural-processes
Nov 2018 - May 2019
Contributions:158 commits, 144 pushes, 1 branch in 6 months
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