Rami Vanguri
Assistant Professor at University of Pittsburgh
Pittsburgh, Pennsylvania, United States
Summary
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Senior🎓
Top SchoolRami Vanguri is an Assistant Professor and computational scientist with nine years of experience developing multimodal machine learning biomarkers across digital pathology, radiology, genomics and electronic health records. He has led interdisciplinary projects at Memorial Sloan Kettering and Columbia that integrate multiplexed imaging, flow cytometry, CT/MRI and sequencing to predict immunotherapy response and characterize tumor microenvironment heterogeneity. His work spans from deep-learning risk models for NSCLC published in Nature Cancer to novel topology-derived spatial biomarkers in breast cancer, reflecting a blend of methodological rigor and clinical translation. Rami’s background in particle physics informs a data-centric, probabilistic approach to complex biomedical problems and a habit of building robust analysis pipelines with small, focused teams. He recently transitioned to NYU Langone, continuing collaborative translational research at the intersection of pathology, oncology and computational science. Colleagues value his ability to bridge clinical questions and machine learning solutions that improve predictive accuracy beyond standard biomarkers.
10 years of coding experience
13 years of employment as a software developer
University of California, San DiegoDoctor of Philosophy (PhD) Elementary Particle Physics, Doctor of Philosophy (PhD) Elementary Particle Physics at University of Pennsylvania
Github Skills (26)
keras9
python8
deep-learning8
imagej8
machine-learning8
qupath7
deep-neural-networks7
tensorflow7
scale7
digital-pathology7
neural-network6
opencv6
javafx6
ml5
image-processing5
Programming languages (6)
Github contributions (5)
rvanguri/psb_2017
Jul 2017 - Aug 2017
Contributions:27 pushes, 1 branch in 14 days
tatonetti-lab/nsides
May 2017 - Aug 2019
Exploring drug-drug interactions on a massive scale.
Contributions:59 commits, 160 pushes, 1 comment in 2 years 3 months
deep-learningmachine-learningscaledrugdeep-neural-networks
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