Justin P. Haldar
University of Southern California
University Park Campus
3740 S. McClintock Avenue
Hughes Aircraft Electrical Engineering Center (EEB) #442, M/C 2564
Los Angeles, CA 90089-2564
jhaldar AT usc DOT edu
Justin Haldar is an Assistant Professor in the Ming Hsieh Department of Electrical Engineering
at the University of Southern California
(USC), where he co-directs the Biomedical Imaging Group
. He is a member of the Signal and Image Processing Institute
, holds a joint appointment in the Department of Biomedical Engineering
, and is affiliated with the Dornsife Cognitive Neuroscience Imaging Center
and the Brain and Creativity Institute
. He received the B.S. and M.S. degrees in electrical engineering in 2004 and 2005, respectively, and the Ph.D. in electrical and computer engineering in 2011, all from the Department of Electrical and Computer Engineering
at the University of Illinois at Urbana-Champaign
His research interests include multidimensional signal processing, biomedical imaging, neuroimaging, magnetic resonance imaging (MRI), constrained image reconstruction, signal modeling, inverse problems, compressed sensing, parameter estimation, and experiment design.
Magnetic resonance (MR) neuroimaging technology has created unprecedented opportunities to unveil the mysteries of the central nervous system, probing scales ranging from organs and systems down to individual cells and molecules, and enabling the visualization and quantification of anatomy, physiology, and metabolism. However, while MR neuroimaging techniques have been developing for decades, many advanced imaging protocols are still impractical for common use due to long data acquisition times, limited signal-to-noise ratio, and various other practical and experimental factors — this limits the amount of information we can extract from living human subjects, despite the known power and flexibility of current MR technology. Our research group addresses such limitations from a signal processing perspective, developing novel methods for data acquisition, image reconstruction, and parameter estimation that combine: (1) the modeling and manipulation of physical imaging processes; (2) the use of novel constrained signal and image models; (3) novel theory to characterize signal estimation frameworks; and (4) fast computational algorithms and hardware.
We are seeking excellent students with a strong background in signal processing, with an interest in developing methods to improve existing advanced MR methods and enable the next generation of imaging-based biomedical and neuroscientific inquiry.