Charles Blackmon-luca
Systems Software Engineer, Accelerated Data Analytics GPU
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolCharles Blackmon-luca is a systems software engineer with nine years of experience building GPU-accelerated data analytics and open-source tooling, currently working on accelerated data analytics at NVIDIA while completing a CS degree with an applied math minor at Columbia. He blends back-end systems work, DevOps and test automation—contributing to high-profile projects like RAPIDS, Dask, cuDF and Numba—to improve GPU CI, performance monitoring, and data-loading reliability. Charles has a proven track record of pragmatic engineering: fixing complex installation and buffer I/O bugs, pinning dependency versions for conda-forge, and adding GPU metrics to distributed system monitors. Equally comfortable updating community notebooks and improving front-end UX, he pairs production-focused engineering with a habit of taking on accelerated coursework and community outreach. Notably, his contributions often sit at the intersection of data science workflows and low-level performance engineering, reflecting an interest in statistical analysis and integrating CS with mathematics.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Science, Computer Science, Minor in Applied Mathematics, 3.55, Bachelor of Science, Computer Science, Minor in Applied Mathematics, 3.55 at Columbia University in the City of New YorkGPA: 102.22, GPA: 102.22 at Valley Stream Central High School
English, Italian
Stackoverflow
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Github Skills (45)
dependency-management10
notebook10
javascript10
pytest10
package-management10
python10
data-science10
signal-processing10
testing10
dataframes10
cudf10
conda10
bash10
io10
cicd10
Programming languages (14)
Github contributions (5)
dask/distributed
Mar 2021 - Jan 2023
A distributed task scheduler for Dask
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:110 reviews, 40 commits, 69 PRs in 1 year 9 months
Contributions summary:Charles contributed to the Dask distributed task scheduler, focusing on improvements to system monitoring and performance reporting. The user added GPU metrics to the system monitor, enabling the collection of GPU utilization and memory usage data. They also integrated system monitoring data into the performance reports, adding a dedicated system tab and including log data for enhanced debugging and analysis. Furthermore, the user addressed code style issues by integrating and applying `isort` to the codebase.
pythonschedulerdaskdistributed-computingdistributed-systems
dask/dask
Jan 2023 - Jan 2023
Parallel computing with task scheduling
Role in this project:DevOps & Test Automation Engineer
DevOps & Test Automation EngineerContributions:154 reviews, 3 commits, 86 PRs in 8 days
Contributions summary:Charles primarily focused on enhancing the testing infrastructure and Continuous Integration/Continuous Deployment (CI/CD) processes for the Dask project. They implemented GPU-related testing by adding pytest markers and integrating GPU CI build scripts. Further contributions include the expansion of test coverage and ensuring the testing frameworks work with the latest dependencies. They also contributed to testing sorting functionalities with custom sort functions.
pythonschedulingparallelnumpydask
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