Samuel Stanton
Member Of Technical Staff at Anthropic
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSamuel Stanton is a machine learning researcher and engineer with nine years of experience building lab-in-the-loop systems and control algorithms for life-sciences applications, currently a Member of Technical Staff at Anthropic. He holds a PhD in Data Science from NYU and has led ML efforts at Genentech, contributing core algorithms for therapeutic antibody lead optimization, and co-founded Coefficient Bio to translate ML into biotech workflows. Samuel has a strong foundations-first approach—publishing and deploying probabilistic and kernel methods—and is an active open-source contributor to gpytorch, where he improved kernel implementations, batching, and device-agnostic lazy tensor behavior. His background spans applied research at AWS and Secondmind, production-focused engineering, and even federally sensitive analytics work, reflecting a rare combination of rigorous theory, hands-on systems building, and domain experience in biology-informed optimization.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.), Applied Mathematics, Bachelor of Science (B.S.), Applied Mathematics at University of Colorado DenverDoctor of Philosophy - PhD, Data Science, Doctor of Philosophy - PhD, Data Science at New York UniversityMaster of Science - MS, Operations Research, Master of Science - MS, Operations Research at Cornell University
Github Skills (4)
gaussian-processes10
python10
pytorch10
unit-testing9
Programming languages (4)
Github contributions (5)
cornellius-gp/gpytorch
Jun 2018 - Jan 2020
A highly efficient implementation of Gaussian Processes in PyTorch
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 reviews, 13 commits, 7 PRs in 1 year 6 months
Contributions summary:Samuel contributed to the `gpytorch` library, a PyTorch implementation of Gaussian Processes. Their work primarily involved modifying the `ConstantMulLazyVariable`, `RBFKernel`, and `LazyTensor` classes, suggesting a focus on kernel design and lazy tensor operations. They fixed batch-mode issues, added a rational quadratic kernel, and implemented a device-agnostic `.to` method for lazy tensors, enhancing the library's functionality and performance. The commits also included unit tests to ensure the reliability of the implemented features.
gaussian-processespytorchgpu-acceleration
samuelstanton/gnosis
Oct 2020 - Dec 2021
Code to reproduce experiments from 'Does Knowledge Distillation Really Work' a paper which appeared in the 2021 NeurIPS proceedings.
Contributions:86 commits, 2 PRs, 6 pushes in 1 year 1 month
information-theorydistillationtransfer-learningensemblebayesian
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