Jeffrey Chiang
Assistant Professor In Residence at David Geffen School of Medicine at UCLA
Los Angeles, California, United States
Summary
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Senior🎓
Top SchoolJeffrey Chiang is an Assistant Professor in Residence at UCLA with a decade of experience applying machine learning, statistics, and big-data methods to questions in human cognition and clinical prediction. He combines a Ph.D. in Computational and Cognitive Psychology with hands-on engineering experience—contributing to open-source neuroimaging tools like nilearn to improve cross-validation and compatibility across scikit-learn versions. His work spans academia and industry, from deep learning for 3D medical imaging at VoxelCloud to predictive modeling for UCLA Health, reflecting strength translating methodological research into practical clinical and operational solutions. He has a track record of mentoring and training students and building reproducible analysis pipelines for behavioral and neuroimaging data. Known for bridging rigorous statistical practice with production-focused code, he brings both experimental design acumen and software engineering discipline to interdisciplinary projects. Based in Los Angeles, he blends curiosity-driven cognitive science research with tangible impact on healthcare analytics.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computational and Cognitive Psychology, Doctor of Philosophy (Ph.D.), Computational and Cognitive Psychology at University of California, Los Angeles
Github Skills (10)
scikit10
python10
neuroimaging10
cross-validation10
scikit-learn10
brain-imaging9
testing9
machine-learning9
decoding8
connectivity8
Programming languages (2)
Github contributions (5)
nilearn/nilearn
Jul 2017 - Jul 2017
Machine learning for NeuroImaging in Python
Role in this project:
Data Scientist
Data ScientistContributions:11 commits, 1 PR, 5 comments in 4 days
Contributions summary:Jeffrey primarily contributed to the `nilearn` project by modifying the `searchlight` and related testing components. Their work focused on improving compatibility with different versions of scikit-learn, particularly addressing issues related to cross-validation and group labels. The commits involved adding features, refactoring code, and adding tests to improve the reliability of the library. Several changes included updating imports, adjusting function calls, and modifying test cases to account for changes in scikit-learn versions.
neuroimagingpythondecodingbrain-connectivitymachine-learning
njchiang/LanguageMVPA
Nov 2015 - Dec 2020
Contributions:58 PRs, 254 pushes, 29 branches in 5 years 2 months
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