Dr. Razlighi obtained his Ph.D. degree in Electrical Engineering/Image processing in 2009, from the University of Texas. During his Ph.D. program, he introduced a new causal Markov random field (MRF) model, Quadrilateral MRF, which has been influential in the medical and commercial image analysis field and so far, it resulted in twelve publications and one patent. Dr. Razlighi’s advanced training on neuroimaging and neuroscience started with one year postdoctoral training at the Brain Imaging Laboratory of the Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, Columbia University. In addition, he had two more years of Postdoctoral training in the Cognitive Neuroscience Division, Department of Neurology, Columbia University. Dr. Razlighi has been involved in several neuroimaging projects concerning the implementation of mathematical models and methods for the analysis of MRI and fMRI data, including but not limited to: (a) the development of a method for extracting brain features related to the cortical folding pattern, (b) developed a new non-stationary MAP-QMRF classifier for brain image segmentation (c) Analysis of fMRI data in subject’s native space which circumvents the problematic spatial smoothing step particularly in studies comparing young and old, (d) Investigate the effects of inter-hemispheric averaging in the analysis of resting-state fMRI data. In addition, He has participated in in neumeros courses in neuroscience and neuroimaging during his postdoctoral training.