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
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
Alzheimer’s diseaseBrain tumorsBreast cancerDeep learningHigh-dimensional data analysis/integrationMachine learningMedical image analysisSpatial/temporal data analysis
A deep learning approach to re-create raw full-field digital mammograms for breast density and texture analysis
(TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma PatientsZhong, L., Li, T., Shu, H., Huang, C., Michael Johnson, J., Schomer, D. F., Liu, H. L., Feng, Q., Yang, W., & Zhu, H.
Volume223AbstractGlioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM integrity with manual delineation of tumors. This paper aims to develop a comprehensive analytical pipeline, called (TS)2WM, to integrate both the superior performance of brain tumor segmentation and the impact of GBM tumors on the WM integrity via tumor segmentation and tract statistics using the diffusion tensor imaging (DTI) technique. The (TS)2WM consists of three components: (i) A dilated densely connected convolutional network (D2C2N) for automatically segment GBM tumors. (ii) A modified structural connectome processing pipeline to characterize the connectivity pattern of WM bundles. (iii) A multivariate analysis to delineate the local and global associations between different DTI-related measurements and clinical variables on both brain tumors and language-related regions of interest. Among those, the proposed D2C2N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project.
D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional DatasetsShu, H., Wang, X., & Zhu, H.
Journal titleJournal of the American Statistical Association
Page(s)292-306AbstractA typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing decomposition methods often focus on the orthogonality between the common and distinctive matrices, but inadequately consider the more necessary orthogonal relationship between the two distinctive matrices. The latter guarantees that no more shared information is extractable from the distinctive matrices. We propose decomposition-based canonical correlation analysis (D-CCA), a novel decomposition method that defines the common and distinctive matrices from the (Formula presented.) space of random variables rather than the conventionally used Euclidean space, with a careful construction of the orthogonal relationship between distinctive matrices. D-CCA represents a natural generalization of the traditional canonical correlation analysis. The proposed estimators of common and distinctive matrices are shown to be consistent and have reasonably better performance than some state-of-the-art methods in both simulated data and the real data analysis of breast cancer data obtained from The Cancer Genome Atlas. Supplementary materials for this article are available online.
Assessment of network module identification across complex diseasesFailed generating bibliography.Abstract
Journal titleNature methods
Page(s)843-852AbstractMany bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.
Estimation of large covariance and precision matrices from temporally dependent observationsShu, H., & Nan, B.
Journal titleAnnals of Statistics
Page(s)1321-1350AbstractWe consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. In particular, the rates of convergence are obtained for the generalized thresholding estimation of covariance and correlation matrices, and for the constrained l 1 minimization and the l 1 penalized likelihood estimation of precision matrix. Properties of sparsistency and sign-consistency are also established. A gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations. As a motivating example, we study the brain functional connectivity using resting-state fMRI time series data with long-range temporal dependence.
Multiple testing for neuroimaging via hidden Markov random fieldShu, H., Nan, B., & Koeppe, R.
Page(s)741-750AbstractTraditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three-dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation-maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer's or another dementia, and normal controls in the FDG-PET imaging study of the Alzheimer's Disease Neuroimaging Initiative.