Our current research focus lies in identifying biomarkers of brain disorders from neuroimaging data, which is an exciting and rapidly growing research area at the intersection of machine learning, biomedical engineering and neuroscience. Conventional approaches towards imaging biomarkers reduce the data dimensionality by averaging the image information to one or few variables of a-priori interest – for example, the volume of Hippocampus for Alzheimers diagnosis. However, such methods discard much information present in brain images. Instead, allowing machine learning algorithms to decide what is important and decipher the predictive pattern (sometimes called statistical biomarker) is projected to be beneficial. This leads to challenging and underconstrained machine learning problems where the data dimensionality is larger than the number of samples and advanced computational techniques are required to solve these problems. We develop these techniques and apply to them to large brain image databases to help neuroscientists to find imaging markers to different brain disorders.