Justin P. Haldar
Associate Professor of Electrical and Computer Engineering and Biomedical Engineering
- Doctoral Degree, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
- Master's Degree, Electrical Engineering, University of Illinois at Urbana-Champaign
- Bachelor's Degree, Electrical Engineering, University of Illinois at Urbana-Champaign
BiographyJustin Haldar is an Associate Professor in the Ming Hsieh Department of Electrical and Computer Engineering. He is a member of the Signal and Image Processing Institute, and holds a joint appointment in the Department of Biomedical Engineering. 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 Vice Chair and Chair-Elect of the IEEE Signal Processing Society's Technical Committee on Computational Imaging. He is also a Senior Area Editor for the IEEE Transactions on Computational Imaging and an Associate Editor for the IEEE Transactions on Medical Imaging.
Research SummaryMagnetic 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."
- 2014 NSF Career Award
- 2011 University of Illinois at Urbana-Champaign M. E. Van Valkenburg Graduate Research Award
- 2010 IEEE ISBI Best Student Paper Award (first author)
- 2010 EEE EMBC Student Paper Competition First-Place Award (first author)
- 2009 ISMRM I. I. Rabi Young Investigator Award (co-author)
- 2009 University of Illinois at Urbana-Champaign Beckman Institute Graduate Fellowship
- 2009 University of Illinois at Urbana-Champaign University of Illinois Fellowship
- 2009 University of Illinois at Urbana-Champaign Electrical and Computer Engineering Distinguished Fellowship