Clayton Rabideau
Founder, CEO at Syntensor
Cambridge, England, United States
Summary
👤
Senior🎓
Top SchoolClayton Rabideau is the founder and CEO of Syntensor, building mechanistic, scalable models of human physiology to make complex biological processes tractable for scientists, drug developers, and clinicians. With a PhD in Chemical Engineering and Biotechnology from the University of Cambridge and nine years of technical experience, he blends deep academic training with hands-on ML engineering. Clayton is an active open-source contributor—his work on neural differential equations in the popular torchdyn project and PyTorch integration/testing in SymPy highlights expertise at the intersection of numerical methods, ML, and scientific computing. Based in Cambridge, he pairs entrepreneurial leadership with a rare fluency in both mechanistic biology and advanced neural ODE/SDE tooling, enabling practical translational impact.
9 years of coding experience
Doctor of Philosophy (Ph.D.), Chemical Engineering and Biotechnology, Doctor of Philosophy (Ph.D.), Chemical Engineering and Biotechnology at University of Cambridge
Github Skills (10)
computer-algebra10
pytorch10
deep-learning10
python10
testing10
test-automation9
mathematics8
neural-network8
math8
release-management7
Programming languages (4)
Github contributions (5)
sympy/sympy
Sep 2020 - Mar 2025
A computer algebra system written in pure Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:5 reviews, 3 PRs, 13 comments in 4 years 6 months
Contributions summary:Clayton contributed significantly to the testing infrastructure and integration of PyTorch support within the SymPy library. Their primary focus was implementing and expanding test cases for PyTorch-related functionalities, as evidenced by the addition of the `test_torch.py` file and subsequent updates. They also updated the testing and release processes by adding a test run file and modifying the tarball creation. The user also removed old code blocks and partial derivative symbols.
mathpythonsciencecomputer-algebra-systemalgebra
DiffEqML/torchdyn
Jul 2020 - Sep 2020
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
Role in this project:ML Engineer
ML EngineerContributions:11 commits, 4 PRs, 1 comment in 2 months
Contributions summary:Clayton contributed significantly to the `torchdyn` repository, focusing on implementing and integrating neural differential equations and related machine learning models. The contributions include creating a PyTorch-geometric version of a Graph DE model, updating and modifying SDE (Stochastic Differential Equations) related files, and creating and implementing a Latent SDE tutorial. These changes demonstrate an expertise in utilizing PyTorch, neural ODEs, and deep learning techniques for various research and practical applications.
dynamical-systemsdifferentialdedicateddifferential-equationsnumerical-methods
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