Brad Buran
Research Assistant Professor at Oregon Health and Science University
Portland, Oregon, United States
Summary
👤
Senior🎓
Top SchoolBrad Buran is a Research Assistant Professor and neuroscientist-engineer with 13 years of experience designing in vivo assays, high-throughput electrophysiology pipelines, and custom hardware/software for behavioral neuroscience. He blends hands-on circuit and microcontroller design (Arduino, NI, Tucker-Davis) with multi-language software engineering (Python, R, C, LabVIEW, Matlab, TCL) to automate experiments and build robust data-acquisition and analysis systems. At OHSU and prior roles he has led small teams, created novel assays for CNS drug screening, and translated vendor tooling via custom wrappers to enable scalable experiments. An active contributor to major open-source projects such as MNE-Python and pandas, he brings production-grade bug fixes, performance improvements and documentation polish to community tools used for EEG/MEG and data analysis. Based in Portland, he pairs MIT-trained mechanistic insight with pragmatic engineering to accelerate translational neuroscience research.
13 years of coding experience
14 years of employment as a software developer
PhD, PhD at Massachusetts Institute of TechnologyB.A., B.S., B.A., B.S. at University of Maryland
English, American Sign Language
Github Skills (19)
ec10
python10
pandas10
el10
numpy10
mage10
neuroscience10
ereg10
ceph10
data-analysis10
data-science9
time-frequency9
machine-learning9
sql8
testing7
Programming languages (1)
Github contributions (5)
mne-tools/mne-python
Jul 2012 - Mar 2014
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits in 1 year 7 months
Contributions summary:Brad primarily contributed to the improvement and maintenance of the MNE-Python library. Their contributions involved bug fixes, such as resolving indentation errors and fixing version numbers for dependencies. They also enhanced functionality by implementing a better progress bar for downloading data and added temporal decimation to the Morlet analysis. Furthermore, they made stylistic changes, including documentation improvements and code cleanup, while improving the code's robustness.
visualizationneuroimagingneurosciencepythonstatistics
pandas-dev/pandas
Mar 2013 - Mar 2014
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits in 11 months
Contributions summary:Brad primarily contributed to improving the pandas library's functionality and error handling. They addressed issues related to parsing errors, enhancing exception specificity for better debugging. Additionally, the user extended the library by adding features such as parameterized queries for database interactions and preserving series names in GroupBy operations. They also modified test files and documentation to reflect the added changes.
pythondatalabeled-datamanipulationdataframes
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