Chao Wu
Principal Imaging Scientist Business Director at MILabs
Houten, Utrecht, Netherlands
Summary
👤
Senior🎓
Top SchoolChao Wu is a Principal Imaging Scientist and Business Director with 12 years of experience translating cutting-edge nuclear imaging research into commercial software and customer solutions at MILabs. Trained at Tsinghua and holding a PhD in Medical Sciences from the University of Groningen, he has deep expertise in ultra-high-resolution quantitative multi-pinhole SPECT and micro-CT, combining algorithm development, CUDA-aware image processing fixes, and hands-on reconstruction toolkit contributions. He bridges R&D, software engineering, sales and support across APAC and Europe, routinely training users and driving product improvements informed by field feedback. Known for debugging complex pipeline issues (including CUDA and ambiguous type problems in RTK), he brings a rare mix of academic rigor and pragmatic engineering that accelerates adoption of advanced imaging systems.
11 years of coding experience
15 years of employment as a software developer
BSc, Electronic Engineering, BSc, Electronic Engineering at Tsinghua UniversityPhD, Medical Sciences, PhD, Medical Sciences at Rijksuniversiteit Groningen / University of Groningen
Chinese, English
Github Skills (6)
cuda10
c-language10
cprogramming-language10
image-processing10
itk9
cmake5
Programming languages (1)
Github contributions (5)
RTKConsortium/RTK
May 2014 - Jan 2019
Reconstruction Toolkit
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 4 PRs, 7 comments in 4 years 8 months
Contributions summary:Chao primarily contributed to bug fixes and code improvements within the reconstruction toolkit. Their work included removing unnecessary code related to FDK recon filters, addressing a compiler error related to ambiguous type definitions, and correcting issues related to image processing within the CUDA framework. These modifications specifically target improving the performance and stability of the reconstruction algorithms.
reconstructionpythonmachine-learning
Hakkk2002/RTK
Feb 2015 - Jan 2019
Reconstruction Toolkit
Contributions:8 pushes, 3 branches in 3 years 11 months
reconstructionpythonmachine-learning
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