Hai Shu

Assistant Professor of Biostatistics

Professional overview

Dr. Hai Shu is an Assistant Professor in the Department of Biostatistics at New York University. He earned a Ph.D. in Biostatistics from University of Michigan and a B.S. in Information and Computational Science from Harbin Institute of Technology in China.

His research interests include high-dimensional data analysis (esp. data integration), machine/deep learning, medical image analysis (e.g., PET, MRI, Mammography), and their applications in Alzheimer’s disease, brain tumors, breast cancer, etc. He has published relevant papers in top-tier journals and conference, such as The Annals of Statistics, Journal of the American Statistical Association, Biometrics, and AAAI Conference on Artificial Intelligence. He has also served as a reviewer on related topics for Journal of the American Statistical Association, Statistica Sinica, International Joint Conference on Artificial Intelligence, etc.

Prior to joining NYU, Dr. Hai Shu was a Postdoctoral Fellow in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center. 

View Dr. Hai Shu's website at https://wp.nyu.edu/haishu

Education

Postdoctoral Fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA

Ph.D. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USA

M.S. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USA

B.S. in Information and Computational Science, Department of Mathematics, Harbin Institute of Technology (哈尔滨工业大学), China

Areas of research and study

Alzheimer’s disease

Brain tumors

Breast cancer

Deep learning

High-dimensional data analysis/integration

Machine learning

Medical image analysis

Spatial/temporal data analysis

Publications

Publications

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

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Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation

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(TS)2WM : Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients

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D-CCA : A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets

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Assessment of network module identification across complex diseases

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Automatic brain tumor segmentation with domain adaptation

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Estimation of large covariance and precision matrices from temporally dependent observations

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Sensitivity analysis of deep neural networks

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A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation

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Multiple testing for neuroimaging via hidden Markov random field

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