Corey Nolet
Senior Data Scientist
Maryland, United States
Summary
🤩
RockstarCorey J. Nolet is a Senior Data Scientist and software architect with 12 years of experience who specializes in GPU-accelerated machine learning and high-performance systems engineering. He contributes to core RAPIDS projects (RAFT, cuML, cuGraph) and CuPy, working across low-level C++ communications (NCCL/UCX), CUDA-accelerated primitives, and test automation to make ML building blocks production-ready. He combines applied data science—EDA, recommender systems, and representation learning—with hands-on systems work like benchmarking cuML against scikit-learn and automating complex build and dependency flows. Based in Maryland, he’s comfortable moving between notebook-level experimentation and the plumbing that scales GPU workloads, a blend that helps translate research ideas into fast, reliable software.
12 years of coding experience
Stackoverflow
96reputation
9kreached
4answers
5questions
Github Skills (50)
asynchronous10
graph-algorithms10
benchmark10
c-language10
parallel10
parallelization10
python10
async10
testing10
scikit10
graph10
sparse-matrix10
ucx10
machine-learning10
benchmarking10
Programming languages (15)
Github contributions (5)
rapidsai/raft
May 2020 - Jan 2023
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1741 reviews, 424 commits, 386 PRs in 2 years 9 months
Contributions summary:Corey's contributions primarily revolve around the implementation of functionalities related to collective and point-to-point communication using NCCL and UCX. Their focus is on the development and integration of communication primitives within the "raft" library, evident from the initial commits that introduced and refined communication functionalities, including implementations for collectives (e.g., allreduce, bcast) and point-to-point communication. They are making low level changes to the `c++` code related to communications infrastructure. These contributions form building blocks for writing high-performance applications.
sciencedata-sciencemachine-learninggraph-data-sciencefundamental
rapidsai/cuml
Dec 2018 - Jan 2023
cuML - RAPIDS Machine Learning Library
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:683 reviews, 2397 commits, 653 PRs in 4 years 2 months
Contributions summary:Corey primarily worked on enhancing the cuML library, focusing on improving the existing code base and ensuring optimal performance. This includes fixing import errors, refactoring code to use more efficient data structures, adding parameters, and addressing issues to ensure the code is compatible with CUDA 11.2 and the latest RAFT libraries. The commits also involved applying improvements for the correct functioning of the library.
cudacumlnvidiadata-sciencegpu
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