Chris Roat
Figuring It Out at Self-employed
Los Altos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChris Roat is a seasoned software engineer and scientific computing specialist with 12 years of professional experience building large-scale data pipelines, analysis infrastructure, and high-performance scientific tools. He has led infrastructure and algorithm work at Google—spanning ads auction systems and biomedical Baseline Study analytics—and later applied that expertise to computational neuroscience at Stanford and open-source projects like Neuroglancer, Dask, scikit-image, and Cellpose. His contributions range from dependency and backend modernization to performance-focused numba/GIL optimizations and robust test automation, showing a blend of pragmatic engineering and deep numerical understanding. Based in Los Altos, he pairs academic rigor (PhD in Experimental Physics from Stanford) with hands-on distributed systems and ML engineering, and he brings a knack for turning physics-grade analysis into production-ready software.
12 years of coding experience
19 years of employment as a software developer
B.S., Applied and Engineering Physics, B.S., Applied and Engineering Physics at Cornell UniversityPhysics, Physics at University of Illinois Urbana-ChampaignPh.D., Experimental Physics, Ph.D., Experimental Physics at Stanford University
Stackoverflow
742reputation
59kreached
13answers
30questions
Github Skills (37)
algorithm10
dependency-management10
algorithms10
set-operations10
pytest10
operation10
python10
image-processing10
testing10
tornado10
machine-learning10
zarr10
scikit-image10
array-merge10
data-structure10
Programming languages (13)
Github contributions (5)
dask/dask
Dec 2019 - Jan 2022
Parallel computing with task scheduling
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:9 reviews, 8 PRs, 112 comments in 2 years 2 months
Contributions summary:Chris contributed to the Dask library, focusing on improvements to array operations and Zarr integration. Their work includes modifying the `to_zarr` function to delay metadata creation, adding the `region` kwarg, and supporting stack operations with unknown chunk sizes. Furthermore, the user improved the rechunking process by implementing balanced rechunking and updating documentation, showcasing proficiency in array manipulation and data storage optimization.
pythonschedulingparallelnumpydask
MouseLand/cellpose
Jun 2020 - Dec 2021
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Role in this project:ML Engineer
ML EngineerContributions:10 commits, 3 PRs, 34 comments in 1 year 6 months
Contributions summary:Chris made significant contributions to the `cellpose` repository by optimizing and refactoring core routines related to the segmentation algorithm. They removed manual garbage collection and released the Global Interpreter Lock (GIL) in numba routines to improve performance in threaded applications. Furthermore, the user added a distributed segmentation library and made changes to model loading and downloading, demonstrating a focus on improving the functionality and efficiency of the cell segmentation process.
loophuman-in-the-loopcell-biologycell-segmentationcapabilities
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