Ted Chang
San Jose, California, United States
Summary
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Rockstar🎓
Top SchoolTed Chang is a performance and software engineer with 12 years of experience focused on cloud and application/infrastructure performance, automation, and Linux/Unix system administration. With a strong academic foundation in computer science, mathematics, and statistics, he moved from bioinformatics support into virtualization and cloud performance work at IBM Research, thriving on solving hard problems with minimal supervision. Ted contributes to prominent open-source projects like Feast, improving CLI and data-path tooling for feature stores used in ML pipelines, and brings practical DevOps experience converting online feature responses into Pandas-ready formats. Known for tenacity and fast learning, he blends statistical rigor with systems-level troubleshooting to drive measurable performance improvements. Based in San Jose, he combines hands-on engineering with a habit of making behind-the-scenes improvements that teammates repeatedly call out as especially useful.
12 years of coding experience
B.S., Computer Science, Mathematics, B.S., Computer Science, Mathematics at Southern Illinois University, Carbondale
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (17)
feature-store10
python10
data-engineering10
command-line-interface10
command-line10
cli10
data-science9
mlops9
pandas8
testing8
data-quality7
big-data7
kubernetes4
dockers4
docker4
Programming languages (12)
Github contributions (5)
feast-dev/feast
Feb 2021 - Aug 2021
The Open Source Feature Store for Machine Learning
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:28 reviews, 22 commits, 15 PRs in 6 months
Contributions summary:Ted primarily contributed to the command-line interface (CLI) functionality of the Feast feature store, adding features like the `chdir` option to change the working directory and validating project and repository names for the `apply` and `init` commands. They also addressed help message formatting and implemented checks for duplicated feature view names during application. Additionally, the user modified file paths related to test warnings and added a function to convert the online feature retrieval response into a Pandas DataFrame.
pythondata-qualitydata-sciencemlmachine-learning
tedhtchang/kuberay
Mar 2023 - May 2024
A toolkit to run Ray applications on Kubernetes
Contributions:84 pushes, 8 branches in 1 year 2 months
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