Gregory Shimansky
Machine Learning Engineer at Intel Corporation
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolGregory Shimansky is a Machine Learning Engineer with 10+ years building high-performance systems in networking, multimedia and low-level programming, now based in Austin. At Intel he accelerates open-source analytics like Modin and OmniSciDB and previously developed a DPDK-speed Network Function Framework for Go, CI/testing infrastructure, and codecs in MediaSDK. He pairs deep systems and performance tuning expertise with practical ML/data engineering, contributing bug fixes and test improvements to widely used projects such as Modin. A longtime open-source practitioner, Gregory’s work often focuses on making complex, performance-sensitive codebases more maintainable and production-ready—evident from his dependency refactors and test-suite fixes across repos.
10 years of coding experience
23 years of employment as a software developer
Master of Science (MS), Computer Science, Master of Science (MS), Computer Science at Московский Государственный Университет им. М.В. Ломоносова (МГУ)Obninsk school number 12Obninsk humanitarian center (school number 6)
English
Stackoverflow
93reputation
9kreached
3answers
2questions
Github Skills (19)
dependency-management10
debug10
pytest10
python10
data-science10
dataframes10
pandas10
dataframe10
go10
analytics9
networking9
golang8
dpdk6
valgrind6
bazel6
Programming languages (11)
Github contributions (5)
aregm/nff-go
Mar 2017 - Mar 2020
NFF-Go -Network Function Framework for GO (former YANFF)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 releases, 516 commits, 361 PRs in 2 years 11 months
Contributions summary:Gregory primarily worked on refactoring and updating import paths for the project's dependencies to reflect the correct GitHub repository structure. They fixed import paths to point to github.com. The user's commits involved modifying various source files, including examples, test files, and core library files, indicating a focus on code maintenance and dependency management within the project's codebase. These changes suggest a role focused on backend development and code quality.
dpdksdnintelcloudmicroservices
modin-project/modin
Mar 2020 - Oct 2021
Modin: Scale your Pandas workflows by changing a single line of code
Role in this project:Data Scientist
Data ScientistContributions:167 reviews, 93 commits, 85 PRs in 1 year 7 months
Contributions summary:Gregory primarily contributed to bug fixes and improvements within the Modin library, a project focused on scaling Pandas workflows. Their work included addressing issues related to DataFrame operations such as `drop_duplicates`, assignment to empty DataFrames, and series to_csv functionality. They also made code formatting changes and imported correct API packages, indicating a focus on code quality and addressing specific issues within the data science library. The user further worked on fixing various tests, particularly for DataFrame and Series objects.
analyticspythonline-of-codedata-sciencedataframe
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.