Rory Mitchell
Senior System Engineer at NVIDIA
Cologne, North Rhine-Westphalia, Germany
Summary
🤩
Rockstar🎓
Top SchoolRory Mitchell is a Senior System Engineer at NVIDIA with a decade of experience building high-performance ML systems and GPU-accelerated back-end features. He holds a PhD in Computer Science and has driven core contributions to flagship open-source projects like XGBoost and RAPIDS cuML, focusing on GPU tree construction, memory optimization, and performance-critical algorithm refactors. Rory’s work spans production-grade engineering and research, from speeding up tree explainers in SHAP to implementing vectorized leaf prediction in FIL, demonstrating a rare blend of algorithmic depth and systems-level pragmatism. Based in Cologne, he pairs rigorous academic training with hands-on contributions that directly improve large-scale ML tooling used across industry and research.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of WaikatoPostgraduate Diploma in Business Administration, Economics, Postgraduate Diploma in Business Administration, Economics at Massey UniversityGraduate Diploma Computer Science, Graduate Diploma Computer Science at University of Waikato
Github Skills (23)
algorithm10
xgboost10
c-language10
shap10
python10
testing10
gpu-programming10
machine-learning10
machine-learning-algorithms10
explainable-artificial-intelligence10
gpu10
lightgbm10
cuda10
cprogramming-language10
cuml10
Programming languages (8)
Github contributions (5)
dmlc/xgboost
Jul 2016 - Oct 2022
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:574 reviews, 168 commits, 459 PRs in 6 years 4 months
Contributions summary:Rory primarily contributed to the development of the GPU accelerated tree construction, which is a core back-end feature of the XGBoost library. The commits focus on implementing and optimising kernels for GPU operations, including the modification of data structures such as bst_gpair and the creation of new algorithms for constructing histograms and finding split points. The work also involved improving memory usage and the design of the GPU accelerated components.
xgboostpythonflinkdaskdataflow
shap/shap
Nov 2020 - Feb 2022
A game theoretic approach to explain the output of any machine learning model.
Role in this project:ML Engineer
ML EngineerContributions:10 reviews, 34 commits, 7 PRs in 1 year 3 months
Contributions summary:Rory's commits primarily focused on optimizing and extending the testing framework for the SHAP library. They reduced test durations within the tree explainer tests and also introduced testing support for GPU-based tree explainers. These changes involved modifying existing tests, adding new test cases for interventional methods and refactoring the test suite. They also added functionality to support interactions.
explaininterpretabilityshapdeep-learningapproach
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