Winston Chen
Postdoctoral Researcher at Mayo Clinic
Madison, Wisconsin, United States
Summary
👤
Senior🎓
Top SchoolWinston Chen is a postdoctoral researcher and machine learning engineer with nine years of experience building deep learning solutions for medical imaging, federated learning, and trustworthy AI. He develops and validates state-of-the-art models—CNNs, Vision Transformers, and diffusion models—for tasks like segmentation, synthetic CT generation, and uncertainty estimation, and collaborates closely with clinicians to deploy transparent, scalable clinical decision-support tools. At Mayo Clinic and previously in the MIMRTL lab, he focused on reliability, robustness, and interpretability in healthcare AI, publishing and presenting on trustworthy models. Beyond academia, he contributes to open-source geospatial tooling—improving GDAL’s netCDF geometry support—demonstrating a knack for bridging research, production engineering, and interdisciplinary systems. Based in Madison, WI, he is actively seeking full-time roles across on-site, hybrid, or remote settings.
9 years of coding experience
7 years of employment as a software developer
Master's degree Machine Learning, Master's degree Machine Learning at University of Wisconsin-MadisonBachelor's degree Electrical and Electronics Engineering, Bachelor's degree Electrical and Electronics Engineering at Southeast University
Github Skills (13)
vector10
spatial10
geo10
c-language10
vector-math10
netcdf10
geospatial10
cprogramming-language10
spatial-data10
testing9
ogr9
python8
raster7
Programming languages (6)
Github contributions (5)
OSGeo/gdal
Jun 2019 - Aug 2019
GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
Role in this project:
Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:6 commits, 6 PRs, 171 comments in 2 months
Contributions summary:Winston primarily focused on improving the GDAL library's netCDF format support, implementing features for reading and writing CF-1.8 encoded geometries. They added support for various geometry types, including points, lines, and polygons, and also addressed issues related to dimension handling. Furthermore, the user was involved in testing and ensuring the correctness of the implemented features, including writing automated tests.
licensedrasteriogeospatialrasterdata-formats
wchen329/gdal
Jul 2019 - Dec 2019
GDAL is an open source X/MIT licensed translator library for raster and vector geospatial data formats.
Contributions:2 PRs, 87 pushes, 7 branches in 5 months
licensedgeospatialrasterdata-formatsh3
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