Qasim Bukhari
Post-Doctoral Associate
43 Vassar Street, Office: 46-5094
Cambridge, Massachusetts 02139
Email: qbukhari@mit.edu
Connect: Website / LinkedIn
Education
Ph.D., Computational Neuroscience; Swiss Federal Institute of Technology (ETH Zurich), 2017
M.S., Computer Science; University of Eastern Finland, 2011
M.S., Physics; University of Granada, 2011
B.E., Electrical Engineering; NED University of Engineering and Technology, 2009
Background
Dr. Bukhari is a computational neuroscientist with a background in Electrical Engineering, Computer Vision and Artificial Intelligence. This decade has witnessed a boom in artificial intelligence and our understanding of the human brain. Dr. Bukhari uses the artificial intelligence tools to help further our understanding of the human brain. He is an expert in signal processing, multivariate pattern recognition and machine learning. His unique contribution to research is the integration of sophisticated engineering tools to ask translational and basic neuroscience questions by combining his expertise in neuroscience and engineering. He has developed software for basic psychology, preclinical and clinical MRI research. In his past work, he used pre-clinical, clinical and basic psychological brain data from fMRI, MRI and DTI to study the relationship between different conditions, identify differences in brain connectivity and investigate the neuroscience basis of those differences, while working under the supervision of world famous neuroscientists including John Gabrieli, Terry Sejnowski and Klaas Enno Stephan.
At MIT, the goal of his research is to develop personalized treatment/medicine for psychiatric patients using neuroimaging data. He uses different neuroimaging techniques (fMRI, MEG, EEG, and DTI) to better predict the treatment response outcome for psychiatric disorders from many alternate treatment options by using machine learning algorithms. He aims to develop a system that can predict the optimal treatment option from alternate therapies for psychiatric disorder patients. He also uses these tools to investigate the relationship between cognitive skills and brain measures.
