Big Data Approaches to Neuroimaging

Advanced machine learning approaches to brain data analysis including network building, parcellation, and brain state decoding

Statistical machine learning

Brain data analysis using signal processing on graphs

Distributed signal processing on networks/graphs in neuroimaging data

Statistical inference, dictionary learning, sparse recovery, matrix factorization, blind source separation methods applied to neuroimaging application

Structured data recovery, e.g., sparse + low-rank matrix factorization, robust PCA, compressive sensing, structured sparsity

Higher order data analysis, e.g. tensor-based approaches to neuroimaging data analysis

Functional/effective Connectivity of evolving networks

Anatomical imaging and structural connectivity

Multimodal (EEG, MEG, MRI, fMRI, PET, NIRS, DTI) neuroimaging data analysis

Joint study of Structural and functional networks via fMRI plus DTI analysis

Study of altered brain networks in neuropsychiatric disorders

Visual scene reconstruction using brain imaging

Dynamic and non-linear time-series analysis