Jamie Townsend
Postdoctoral Researcher, AMLab at University of Amsterdam
Amsterdam, North Holland, Netherlands
Summary
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Rockstar🎓
Top SchoolJamie Townsend is a postdoctoral researcher at the University of Amsterdam’s AMLab with a decade of experience bridging machine learning research and production-grade software engineering. Funded by competitive personal grants (Dutch Veni and an MSCA fellowship), Jamie focuses on automatic differentiation, probabilistic modeling, and neural network libraries, contributing substantive improvements to flagship projects like JAX, TensorFlow Probability, Flax, and NumPy. Their open-source work includes implementing JVPs for QR decomposition, adding distributions and batching for special functions, and extending convolutional modules—efforts that directly improve performance and correctness in widely used ML toolchains. Prior roles include senior research work at UCL and internships on Google Brain teams, reflecting a blend of academic rigor and industry-scale engineering. Jamie holds a PhD in Computer Science from UCL and an MMath from Oxford, combining strong mathematical foundations with practical systems expertise. Colleagues describe them as a careful implementer who surfaces subtle numerical edge cases while shipping robust, well-tested library code.
10 years of coding experience
MMath, Mathematics, First Class, MMath, Mathematics, First Class at University of OxfordUniversity College London
Github Skills (24)
convolutional-neural-networks10
probabilistic-programming10
python10
testing10
statistics10
machine-learning10
flax10
numpy10
automatic-differentiation10
tensorflow10
autograd10
neural-network10
jax10
linear-algebra10
theano10
Programming languages (9)
Github contributions (5)
HIPS/autograd
Feb 2016 - Sep 2022
Efficiently computes derivatives of NumPy code.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 reviews, 159 commits, 119 PRs in 6 years 8 months
Contributions summary:Jamie primarily focused on enhancing the functionality of the `autograd` library, which efficiently computes derivatives of NumPy code. Their work involved refactoring and extending existing features, such as implementing the Jacobian-vector product and improving the handling of matrix operations within the automatic differentiation framework. They also addressed bugs and edge cases related to specific matrix shapes and computations. The user's contributions involved refactoring code and extending existing functionality.
pythonautomatic-differentiationnumpyautodiffderivatives
pymanopt/pymanopt
Nov 2015 - Oct 2019
Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 233 commits, 35 PRs in 4 years
Contributions summary:Jamie implemented and refined the core infrastructure for optimization on Riemannian manifolds using Python. Their work included the initial setup and implementation of essential mathematical components like the Stiefel manifold. Key contributions involved integrating gradient descent with a line-search solver for optimization problems. The user also added and improved examples that demonstrate the use of different algorithms, such as finding the dominant invariant subspace for a given matrix, with a focus on the Grassmann manifold.
pythonautomatic-differentiationdifferentiationtoolboxoptimization
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