Ray Razlighi Laboratory

In 2009, Dr. Qolamreza "Ray" Razlighi earned his doctorate in electrical engineering and image processing from the University of Texas. During this time, Dr. Razlighi introduced a new causal Markov random field (MRF) model—Quadrilateral MRF (QMRF)—which has dramatically influenced medical and commercial image analysis, resulting in 12 publications and one patent to date. Dr. Razlighi’s advanced knowledge of neuroimaging and neuroscience started with one year of postdoctoral training at the Brain Imaging Laboratory of the Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, Columbia University. His education continued with two years of postdoctoral training in the Cognitive Neuroscience Division, Department of Neurology, Columbia University. 

Dr. Razlighi has been involved in many neuroimaging projects focused on implementing mathematical models and methods for magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) data analysis. These include the development of a method for extracting brain features related to the cortical folding pattern; the development of a new non-stationary maximum a posteriori (MAP) QMRF (MAP-QMRF) classifier for brain image segmentation; analysis of fMRI data in a subject’s native space (thereby circumventing the problematic spatial-smoothing step, particularly in studies comparing young/old); and the investigation of inter-hemispheric averaging in resting-state fMRI data analysis. During his postdoctoral training, Dr. Razlighi participated in numerous neuroscience and neuroimaging courses.  

Hani Hojjati, Ph.D., is a postdoctoral associate in the Weill Cornell Medicine Department of Radiology. In 2011, Dr. Hojjati earned his  bachelor’s degree in electrical engineering from the Mazandaran University of Iran; from 2011 to 2013, he earned his  master's degree in electrical engineering from the Babol Noshirvani University of Technology (NIT); and from 2014 to 2018, he continued his education at NIT, earning a doctorate in electrical engineering.  

At NIT, Dr. Hojjati’s thesis focused on predicting Alzheimer's disease (AD) using multimodal neuroimaging methods and machine learning approaches. By employing neuroimaging and multimodal forecasting techniques, he determined accurate predictors for identifying mild cognitive impairment (MCI) versus AD. Using resting-state functional and structural magnetic resonance imaging (MRI) as tools, he enhanced the accuracy of predicting AD.  

In 2019, Dr. Hojjati started one year of postdoctoral neuroimaging training at the University of Tennessee Health Science Center Department of Pediatrics. In 2020, he entered a second postdoctoral training program at the Weill Cornell Medicine Department of Radiology. His current research involves using multimodal neuroimaging methods, including structural MRI, and amyloid/tau positron emission tomography (PET), to understand the association between neurodegeneration and amyloid/tau pathologies in the brains of both healthy control and MCI subjects. An award-winning researcher, Dr. Hojjati has published more than 12 journal articles and book chapters and presented at 19 conferences.  

From 2010 to 2014, while pursuing his bachelor’s degree in biomedical engineering at the National Institute of Technology in Orissa, India, Dr. Nayak became interested in biomedical signal processing research. From 2014 to 2020, he pursued this path, earning a doctorate in interdisciplinary neuroscience from the National Cheng Kung University of Tainan, Taiwan. In June 2021, he became a postdoctoral researcher in the Weill Cornell Medicine Department of Radiology.  

While earning his Ph.D., Dr. Nayak, who won numerous travel awards to international conferences, used electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) to study the role of emotional processes on response inhibition. In addition to EEG and fMRI, he is interested in numerous biomedical signals, including electrocardiogram (ECG) and electromyography (EMG). Dr. Nayak, who has worked on linking heart-rate variability (HRV) with EEG, aims to better understand the human brain’s cognitive processes by integrating multimodal imaging technologies.  

As a postdoctoral researcher, Dr. Nayak studies the relationship between glucose metabolism as measured by fluorodeoxyglucose (FDG) positron emission tomography (PET) (FDG-PET) and the activation and deactivation of the blood-oxygenation-level dependent (BOLD) signal. When he is not researching, Dr. Nayak enjoys taste-testing coffee variants and gorging on food from around the world.