Christopher Bunn
ML Systems Engineer at Atlassian
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolChristopher Bunn is an ML Systems Engineer with a decade of experience building production-ready machine learning and AutoML tooling, currently at Atlassian after several technical roles at Alteryx. He has deep hands-on experience in model evaluation and selection—contributing to EvalML by implementing new regression objectives and scoring/plotting features—and helped develop Alteryx’s AiDIN Copilot. His background blends computer engineering and economics with an MS in Computer Science from UT Austin, giving him a pragmatic view of building scalable ML systems that meet business needs. Comfortable across research and product teams, he has a history of shipping low-level performance work (HPC/GPU and embedded systems) as well as high-level AutoML features, which helps him bridge research prototypes and reliable production services.
10 years of coding experience
8 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at The University of Texas at AustinBachelor of Science - BS Computer Engineering, Bachelor of Science - BS Computer Engineering at Northeastern University
English
Github Skills (14)
machine-learning10
eval10
automl10
variable-selection10
python10
evaluation10
multiple-selection10
data-science10
feature-selection10
scikit-learn9
feature-engineering9
scikit9
hyperparameter-tuning8
data-analysis8
Programming languages (7)
Github contributions (5)
alteryx/evalml
Sep 2019 - Jan 2023
EvalML is an AutoML library written in python.
Role in this project:
Data Scientist
Data ScientistContributions:3 releases, 265 reviews, 243 commits in 3 years 4 months
Contributions summary:Christopher primarily contributed to the EvalML AutoML library by adding and modifying features related to model evaluation and selection. Their work included adding support for new regression objectives, such as MSE and MaxError, and incorporating them into the AutoML search process. They also refactored the codebase to integrate the functionality for plotting the iteration scores versus the objective values in the AutoML model fit. Furthermore, they added new metrics and fixed existing bugs related to the scoring process.
pythondata-sciencemodel-selectionoptimizationmachine-learning
christopherbunn/NICE
Feb 2017 - Mar 2018
Contributions:3 PRs, 115 pushes, 30 branches in 1 year 1 month
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.