Daniel Perry
Software Engineer at Google
New York City Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolDaniel Perry is a software engineer with a decade of experience building infrastructure and ML tooling, currently at Google after four years on Azure compute at Microsoft. An MIT-trained computer engineer with minors in economics and mathematics, he blends rigorous technical foundations with quantitative finance experience from internships at Bridgewater and D. E. Shaw. He has practical ML contributions in the popular lucidrains/imagen-pytorch repo, improving image sampling diagnostics and intermediate-image visualization to make model behavior more interpretable. Comfortable across systems, research, and production, Daniel has a track record of turning experimental pipelines into reliable services and debugging complex data flows at scale. Based in the New York City area, he pairs deep technical craft with an uncommon mix of finance and policy-facing internships that inform pragmatic, risk-aware engineering.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree Major in Computer Engineering and Minors in Economics and Mathematics, Bachelor's degree Major in Computer Engineering and Minors in Economics and Mathematics at Massachusetts Institute of Technology
Github Skills (6)
pytorch10
text-to-image10
artificial-intelligence10
deep-learning10
python10
computer-vision9
Programming languages (9)
Github contributions (5)
lucidrains/imagen-pytorch
Jun 2022 - Jun 2022
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 2 PRs, 10 comments in 1 day
Contributions summary:Daniel made several contributions focused on improving the image sampling process within the Imagen PyTorch implementation. These contributions included adding functionality to visualize intermediate image results during the sampling process and ensuring compatibility with the `return_pil_images` option. The user also made refinements to the code to fix bugs and ensure the proper handling of intermediate images. These changes improve the user's ability to understand the model's behavior and debug issues, enhancing the overall utility of the library.
pytorchdeep-learningtext-to-videotext-to-imageneural-network
Netruk44/stylegan2-pytorch
Apr 2021 - Dec 2021
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
Contributions:4 PRs, 87 pushes, 29 branches in 7 months
pytorchstyleganartgenerative-artdeep-learning
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