Statistical Inference for High Dimensional Models with Applications to Imaging Genetics

The goal of this project is to develop statistical inference for high-dimensional models. This project is motivated by many studies in brain science. In particular, the intern will apply current and new methods for identifying neurological features associated with certain genetic traits. The project will address questions such as • Do subjects with similar genetic information tend to have similar brain imaging features? • what group of features extracted from biological data can help on disease diagnosis These questions will be addressed under the context of high-dimensional testing problem. Specific Tasks: (1) Read and summarized assigned papers. (2) Exploratory analysis of the data (3) Implementation of the methods using R to analyze the data (4) Submit report, poster, codes. Potential Impact: This project serves the Health and Welnness priority of the Kingdom. The methods will be refined and recalibrated for future application to data on the Saudi population. The methods here will not be confined only to imaging genetics. They can be used to study associations between factors and various diseases including those that are the priority of the kingdom.

The intern is expected to (1.) submit codes that were used in the analysis of the EEG-genetic data; (2.) paper that is of publishable quality using the template of the journal Annals of Applied Statistics; (3.) poster to be printed; (4.) seminar presentation.