Suprateek  Kundu

My research is focused on developing practical statistical methods and theory motivated by high-dimensional biomedical applications in  epidemiology,  neuroimaging,  and  -omics  data. I work at the interface of statistics, machine learning, and computer science to develop efficient and scalable solutions to complex problems involving data from multiple sources and leveraging prior biological knowledge from existing databases or expert knowledge. Some key areas I currently focus on include high dimensional graphical models and variable selection. Applications include inferring brain networks using fMRI data as a function of clinical, demographic and genetic information, inferring dynamic brain networks which change over time, structural connectivity problems entailing analysis of complex diffusion MRI data, and integrative approaches for imaging genetics involving multimodal data. I also work on Deep Learning algorithms for solving non-linear biomedical problems. I am the Director of the Data Analytics and Biostatistics (DAB) core in the Department of Medicine (DOM), where I serve in a leadership role to establish collaborations. 

For further details please visit: https://sites.google.com/view/suprateek/home