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Justin P. Haldar

University of Southern California
University Park Campus
3740 McClintock Avenue
Hughes Aircraft Electrical Engineering Center (EEB) #442, M/C 2564
Los Angeles, CA 90089-2564
(213) 740-2358
https://mr.usc.edu/
jhaldar AT usc DOT edu

Biographical Information

Justin Haldar is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), where he co-directs the Biomedical Imaging Group and currently serves as the Director of the longstanding Signal and Image Processing Institute. He holds a joint appointment in the Department of Biomedical Engineering, and is affiliated with the Dornsife Cognitive Neuroscience Imaging Center, the Brain and Creativity Institute, and the Dynamic Imaging Science Center. 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 computational imaging, inverse problems, magnetic resonance imaging (MRI), constrained image reconstruction, parameter estimation, and experiment design.

His work has been recognized with honors such as the NSF CAREER Award, the IEEE ISBI best paper award, and the IEEE EMBC first-place student paper award, among others. He is the current Chair of the IEEE Signal Processing Society's Technical Committee on Computational Imaging. He is also Deputy Editor-in-Chief for the IEEE Transactions on Computational Imaging, an Associate Editor for the IEEE Transactions on Medical Imaging, and a Deputy Editor for Magnetic Resonance in Medicine.

Curriculum Vitae (CV)

Research Description

Magnetic resonance imaging (MRI) technologies provide unique capabilities to probe the mysteries of biological systems, and have enabled novel insights into anatomy, metabolism, and physiology in both health and disease. However, while MRI is decades old, is associated with multiple Nobel prizes (in physics, chemistry, and medicine), and has already revolutionized fields like medicine and neuroscience, current MRI methods are still very far from achieving the full potential of the MRI signal. Specifically, modern MRI methods suffer due to long data acquisition times, limited signal-to-noise ratio, high monetary costs, and various other practical and experimental limitations — this limits the amount of information we can extract from living human subjects, and often precludes the use of advanced experimental methods that could otherwise increase our understanding by orders-of-magnitude. 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. Methods we developed have enabled substantial acceleration of routine modern MRI exams, and have also enabled the development of highly-informative next-generation MRI experiments that were previously impractical. Our approaches are often based on jointly designing data acquisition and image reconstruction methods to exploit the inherent structure that can be found within high-dimensional data, and we do our best to take full advantage of the "blessings of dimensionality" while mitigating the associated "curses."

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 an interest in enabling/exploring innovative next generation imaging approaches.

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