Ruiming Cao
Research Scientist at Adobe
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolRuiming Cao is a research scientist and computational imaging specialist with nine years of experience spanning optical system design, inverse problems, and biomedical imaging. Currently at Adobe after a stint at the Chan Zuckerberg Imaging Institute, he completed a PhD in Bioengineering at UC Berkeley where he developed novel microscopy and multi-dimensional imaging systems that tightly couple hardware and algorithm design. His work blends optics, physical modeling, and interpretable machine learning to extract previously inaccessible information for healthcare applications, building on earlier MRI and prostate cancer AI research at UCLA. Ruiming’s background in computer science and applied mathematics underpins a practical bent toward optimization-based designs and dynamic computational imaging, and he has industry research experience at Google and Meta focused on applied science and human-computer interaction. Colleagues describe him as someone who bridges lab-grade instrumentation and deployable computation, favoring joint system thinking over isolated algorithmic fixes.
9 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Bioengineering and Biomedical Engineering, Doctor of Philosophy - PhD, Bioengineering and Biomedical Engineering at University of California, BerkeleyMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of California, Los Angeles
Github Skills (14)
motion-tracking9
inverse-problems8
dynamics8
imaging7
denoising7
gradient-descent7
image-reconstruction6
noise6
firmware5
arduino4
computer-vision3
microcontroller3
fourier-transform2
jax1
Programming languages (2)
Github contributions (5)
rmcao/CalCIL
Feb 2022 - Jan 2023
gradient descent helper for computational image reconstruction
Contributions:9 releases, 10 commits, 27 pushes in 11 months
gradient-descentimage-reconstruction
rmcao/nstm
Jun 2024 - Nov 2025
🚀⏱️ Official implementation for "neural space-time model for dynamic multi-shot imaging"
Contributions:1 release, 12 pushes, 1 tag in 1 year 5 months
imagingcomputational-imagingdynamicsimplicit-neural-representationinverse-problems
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