Lindsey Gray
Scientist at Fermilab
Chicago, Illinois, United States
Summary
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Senior🎓
Top School
Top expert inHigh-Performance Machine Learning Computing
Lindsey Gray is a Fermilab staff scientist with 14+ years on the CMS experiment and over 15 years of C++ development experience, blending deep physics analysis with large-scale detector software design. She has led measurements of the Standard Model, searches for new physics, and steered the international effort to build a precision timing detector for the High-Luminosity LHC, delivering picosecond-level timing across millions of channels. Her work spans hands-on algorithm and reconstruction development to systems engineering and team leadership, including contributions to automation and build systems (conda-forge) and performance-focused ML tooling (PyTorch Geometric). Based in Chicago, she pairs a PhD in high energy particle physics with practical engineering that moves cutting-edge instrumentation from concept to production.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, High Energy Particle Physics, Doctor of Philosophy - PhD, High Energy Particle Physics at University of Wisconsin-MadisonBachelor of Science - BS, Physics and Mathematics, Bachelor of Science - BS, Physics and Mathematics at University of Florida
Stackoverflow
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248reached
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Github Skills (24)
dependency-management10
pytorch10
conda-forge10
python10
gnn10
configuration-management10
distributed-computing10
deep-learning10
compile10
logging10
graph-convolutional-networks10
graph-neural-network10
jit10
cms9
build-system9
Programming languages (11)
Github contributions (5)
conda-forge/staged-recipes
Sep 2020 - Nov 2021
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
Role in this project:
Automation Engineer / Build & Release Engineer
Automation Engineer / Build & Release EngineerContributions:5 reviews, 21 commits, 6 PRs in 1 year 1 month
Contributions summary:Lindsey's commits primarily focus on modifying and updating a patch file related to the `fastjet` recipe within the conda-forge staged-recipes repository. These commits involve adjusting compiler flags and build prefixes, and also include attempts to resolve issues related to the configuration and build process. The user appears to be debugging the recipe, identifying and correcting settings related to the build environment for a package.
placeconda-forgerecipescondasubmit
pyg-team/pytorch_geometric
Mar 2020 - Jun 2020
Graph Neural Network Library for PyTorch
Role in this project:ML Engineer
ML EngineerContributions:36 commits, 8 PRs, 41 comments in 2 months
Contributions summary:Lindsey's contributions primarily focused on enhancing the jittability and compatibility with the `torch.jit` module for various graph neural network (GNN) layers within the PyTorch Geometric library. They implemented the `.jittable()` method for several convolutional layers, including EdgeConv, SignedConv, GINConv, GINEConv, DNAConv, PointConv, ChebConv, AGNNConv, GraphConv, SGConv, SplineConv, RGCNConv, TAGConv, NNConv, PPFConv, GATConv, GCNConv, SAGEConv, GatedGraphConv, CGConv, APPNP, GMMConv, FeaStConv, and DynamicEdgeConv, demonstrating a strong understanding of the library's architecture and JIT compilation techniques. This work enables optimized model execution and deployment.
pytorchgraph-convolutional-networksgeometric-deep-learningdeep-learningneural-graph
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