Ke Yu
Contact:
About Me
I am a Research Engineer at Google, working on generative AI for 3D.
I got my Ph.D. from Intelligent Systems at the University of Pittsburgh, advised by Prof. Kayhan Batmanghelich. My doctoral research focused on developing sample-efficient representation learning methods, particularly for medical applications.
Before that, I received M.S. in statistics from Georgia Institute of Technology and had seven years of experience in industry.
Education
Publications
* indicates equal contribution
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Anatomy-guided weakly-supervised abnormality localization in chest x-rays
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Boosting the interpretability of clinical risk scores with intervention predictions
KDD 2022, DSHealth Workshop /
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Hierarchical amortized GAN for 3D high resolution medical image synthesis
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De novo prediction of cell-drug sensitivities using deep learning-based graph regularized matrix factorization
PSB 2022 /
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Extracting disease-relevant features with adversarial regularization
BIBM 2021 /
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Can contrastive learning avoid shortcut solutions?
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Conext Matters: Graph-based Self-supervised Represenation Learning for Medical Images
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Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space
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Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
NeruIPS 2020, Medical Imaging Workshop /
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Hyperbolic Molecular Representation Learning for Drug Repositioning
NeruIPS 2020, ML4Molecules Workshop /
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Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
NeruIPS 2020, Medical Imaging Workshop /
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Monitoring ICU mortality risk with a long short-term memory recurrent neural network
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Monitoring ICU mortality risk with a long short-term memory recurrent neural network
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