Dylan Paiton
Oakland, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDylan Paiton is a Senior AI Applied Scientist with 14 years of experience building neural network models grounded in computational neuroscience and shipping data-intensive ML systems in production. He combines research rigor from a PhD and postdocs with hands-on engineering—designing high-throughput Rust pipelines, globally distributed GCP deployment tooling, and RL-driven security and smart-contract auditing systems. Dylan has led teams from research to product at startups and crypto firms, authored open-source tooling for transaction-level simulation, and contributed ML optimizations such as improving style-transfer implementations. Known for bridging theory and deployment, he emphasizes robustness, representation learning, and adversarially informed architectures. Based in Oakland, he’s an intrinsically motivated collaborator who translates neuroscience insights into practical loss functions and systems that scale.
14 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Vision Science, Doctor of Philosophy (Ph.D.), Vision Science at University of California, BerkeleyBachelor of Science (B.S.), Electrical and Electronics Engineering, Bachelor of Science (B.S.), Electrical and Electronics Engineering at New Mexico Institute of Mining and Technology
English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
machine-learning10
caffe10
python10
style-transfer10
image-processing10
numpy9
scipy9
computer-vision9
algorithms7
Programming languages (8)
Github contributions (5)
fzliu/style-transfer
Sep 2015 - Sep 2015
An implementation of "A Neural Algorithm of Artistic Style" by L. Gatys, A. Ecker, and M. Bethge. http://arxiv.org/abs/1508.06576.
Role in this project:
ML Engineer
ML EngineerContributions:16 commits, 10 PRs, 9 comments in 15 days
Contributions summary:Dylan primarily contributed to the implementation of style transfer functionality within the repository. Their work involved adding new features for image output scaling and directory creation. They optimized the code by using scipy gemm calls and modified model weights to improve performance. The user also addressed code merge conflicts and added a progress bar, enhancing the overall user experience.
arxivabsdeep-learningartisticneural-algorithm
dpaiton/dotfiles
Jun 2018 - Jun 2023
Contributions:30 pushes, 3 branches in 5 years
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