Robert Stone
Data Scientist
Fremont, California, United States
Summary
👤
Senior🎓
Top SchoolRobert Stone is a data scientist and seasoned software engineer with 18 years of experience building scalable, high-availability systems and streamlining processes across security and DevOps domains. He blends deep systems expertise in Linux, C/C++, Perl, and PostgreSQL with practical machine learning applied to production problems, from document clustering to outlier detection. Robert has driven storage and storage-partitioning improvements, deduplication and encryption strategies, and moved monolithic databases to partitioned and alternative layers for major scalability gains. He’s contributed to prominent open-source projects like MXNet and SWIG, improving Perl bindings, metrics, and test coverage for widely used ML and language-bridging tools. Based in Fremont, CA, he pairs hands-on backend engineering with a knack for operationalizing research into reliable products, and holds a BS in Computer Science from UC Santa Cruz. Notably, his work at NTT Application Security yielded multiple patents for automation and pattern-recognition in continuous web-app scanning.
18 years of coding experience
22 years of employment as a software developer
De Anza CollegeUniversity of California Santa Cruz
Github Skills (13)
mxnet10
perl10
ndarray10
metric10
test-automation10
testing9
deep-learning9
c-language8
cprogramming-language8
algorithms6
algorithm6
data-structures6
data-structure6
Programming languages (10)
Github contributions (5)
swig/swig
Aug 2008 - Dec 2018
SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:95 commits, 8 PRs, 45 comments in 10 years 5 months
Contributions summary:Robert primarily contributed to the SWIG project by adding and modifying test cases, specifically for Perl. These changes focused on improving the testing suite to cover various aspects of the Perl bindings generated by SWIG. The contributions include the addition of new test scenarios and adjustments to existing tests, aiming to enhance the robustness and reliability of the Perl integration.
cppwindowshigh-levellinuxprogramming-languages
apache/mxnet
Sep 2017 - Nov 2019
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 commits, 8 PRs, 31 comments in 2 years 2 months
Contributions summary:Robert's contributions primarily involved bug fixes and enhancements to the Perl bindings for the MXNet deep learning framework. They focused on improving the functionality of NDArray and KVStore operations, reducing buffer copies, and implementing utility functions like a Python-style `zip` function. The user also added a new multiclass-MCC metric and refined existing metrics within the Perl environment, demonstrating a solid understanding of the framework's core components and data manipulation.
pythonschedulerdataflowmutationdata-science
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