New York University · Mathematics & StatisticsShu Machine Learning Lab
Hai Shu · New York, New York
Research Focus
Hai Shu's lab focuses on innovative statistical machine learning approaches to improve the diagnosis and prediction of Alzheimer's Disease. Their research uses multi-view data, such as brain images and genetic information, to uncover new insights and biomarkers associated with Alzheimer's. This work aims to provide better tools for early detection, which can lead to more effective treatments and improved patient outcomes.
Alzheimer's Diseasemachine learningbiomarkersneurogeneticsdata analysis
Research Aims
4AIM 01Develop novel multi-view data decomposition methods for brain and genomic networks
AIM 02Create advanced statistical techniques for detecting significant differences in high-dimensional data
AIM 03Implement deep learning frameworks for improved diagnosis and survival predictions in Alzheimer's Disease
AIM 04Apply methods to large-scale datasets and create user-friendly software packages for broader use.
Skills the Lab Values
statistical machine learningmulti-view data analysisgenomic data analysisdeep learning techniquessurvival analysis
Output against the department
Publications46bottom 4% of dept.126th of 131
this lab
most publishedleast published
Citations586bottom 3% of dept.127th of 131
this lab
most citedleast cited
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