Hai Shu (舒海)

Assistant Professor
Department of Biostatistics
School of Global Public Health
New York University
Email: hs120 at nyu dot edu

Biography:
Hai’s research focuses on High-dimensional statistics (esp., data integration), Machine learning, Deep learning (Artificial Intelligence), Neuroimaging data analysis, Genomic data analysis, Alzheimer’s disease, Brain tumors, Breast cancer.

His papers have been published/accepted in top-tier journals/conferences such as Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Biometrics, NeuroImage, IEEE Transactions on Medical Imaging, Medical Image Analysis, Nature Methods, NeurIPS, AAAI, and MICCAI.

He has also served as a reviewer for JASA, JRSSB, Biometrika, Biometrics, Pattern Recognition, Artificial Intelligence in Medicine, AAAI, IJCAI, KDD, etc.

His research is partially supported by NIH grant R21AG070303 (Role: PI) and NYU Discovery Research Fund (Role: Co-PI).

Education & Training:

• Postdoctoral Fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA, 2016-2019
• Ph.D. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USA, 2012-2016
• M.S. in Biostatistics, Department of Biostatistics, University of Michigan, Ann Arbor, USA, 2010-2012
• B.S. in Information & Computational Science, Department of Mathematics, Harbin Institute of Technology (哈尔滨工业大学), China, 2006-2010

News:

• 8/2024: Hai gave an invited talk at IMSI Workshop “Challenges in Neuroimaging Data Analysis”.
• 8/2024: As a Co-PI, Hai received an NYU Discovery Research Fund for Human Health Planning Awards for a project entitled “Enhancing Speech Therapy Through Multimodal Artificial Intelligence”.
• 6-7/2024: Hai served as a faculty mentor at NYU’s Pathways Into Quantitative Aging Research (PQAR) Summer Program.
• 6/2024: Hai gave an invited talk at ICSA China Conference.
• 6/2024: Hai gave an invited talk at ICSA Applied Statistics Symposium.
• 6/2024: Hai gave an invited talk at ICSA-Canada Chapter Symposium.
• 5/2024: Hai gave an invited talk at the 37th New England Statistics Symposium.
• 1/2024: Hai’s PhD student Taehyo’s paper “DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data” was accepted by AISTATS 2024 and also won a Runner-up Award of 2024 ASA Statistics in Imaging Section’s student paper competition.
• 9/2023: Hai’s collaborative paper “Multi-scale Tokens-Aware Transformer Network for Multi-region and Multi-sequence MR-to-CT Synthesis in A Single Model.” was accepted by IEEE Transactions on Medical Imaging.
• 9/2023: Hai’s co-authored paper “K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing” was accepted by NeurIPS.
• 9/2023: Hai’s collaborative paper “A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning” was published in Medical Image Analysis.
• 8/2023: Hai received the NYU GPH Goddard Award.
• 8/2023: Hai organized an invited-paper session “Recent advances in high-dimensional data integration methods and applications” at JSM.
• 7/2023: Hai gave an invited talk at ICSA International Conference.
• 7/2023: Hai gave an invited talk at ICSA China Conference.
• 6/2023: Hai gave an invited talk at ICSA Applied Statistics Symposium.

Openings:
Hai is looking for highly motivated graduate students for Research Assistant positions. Students with strong math/stat background & computational skill (e.g., Python, Matlab, R) and experience in machine/deep learning & brain image analysis are preferred. Please contact by email.