Hai Shu
Hai Shu
Assistant Professor of Biostatistics
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Professional overview
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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
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Education
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Postdoctoral Fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USAPh.D. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USAM.S. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USAB.S. in Information and Computational Science, Department of Mathematics, Harbin Institute of Technology (哈尔滨工业大学), China
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Areas of research and study
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Alzheimer’s diseaseBrain tumorsBreast cancerDeep learningHigh-dimensional data analysis/integrationMachine learningMedical image analysisSpatial/temporal data analysis
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Publications
Publications
Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI
Failed retrieving data.CorrDA: correlation-matrix driven discriminant analysis
Failed retrieving data.Unleashing Diffusion and State Space Models for Medical Image Segmentation
Failed retrieving data.Conditional Diffusion Models Based Conditional Independence Testing
Failed retrieving data.Enhancing missing data imputation through combined bipartite graph and complete directed graph
Failed retrieving data.NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images
Failed retrieving data.UKAN-EP: enhancing U-KAN with efficient attention and pyramid aggregation for 3D multi-modal MRI brain tumor segmentation
Failed retrieving data.Comments on : Data integration via analysis of subspaces (DIVAS)
Failed retrieving data.DeepFDR : A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Failed retrieving data.Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model
Failed retrieving data.A generic fundus image enhancement network boosted by frequency self-supervised representation learning
Failed retrieving data.Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-At-Risk
Failed retrieving data.Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network
Failed retrieving data.K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing
Failed retrieving data.QACL : Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration
Failed retrieving data.Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data
Failed retrieving data.United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration
Failed retrieving data.A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction
Failed retrieving data.Big Data and Machine Learning in Oncology
Failed retrieving data.BiTr-Unet : A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation
Failed retrieving data.CDPA : Common and distinctive pattern analysis between high-dimensional datasets
Failed retrieving data.D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.
Failed retrieving data.mFI-PSO : A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Failed retrieving data.Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement
Failed retrieving data.A deep learning approach to re-create raw full-field digital mammograms for breast density and texture analysis
Failed retrieving data.