Associate Professor of Electrical Engineering-Systems and Biomedical Engineering
- Doctoral Degree, Electrical Engineering, University of Illinois at Urbana-Champaign
- Doctoral Degree, 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
Justin Haldar is an Associate Professor in the Ming Hsieh Department of Electrical 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 focuses primarily on the development of new data acquisition and signal processing methods for improved and accelerated magnetic resonance neuroimaging. His work has been recognized with a number of awards, including a 2014 NSF CAREER award and best paper awards at the 2010 International Symposium on Biomedical Imaging and the 2010 Annual International Conference of the IEEE Engineering in Medicine in Biology Society.
Multidimensional Signal Processing, Biomedical Imaging, Neuroimaging, Magnetic Resonance Imaging, Constrained Image Reconstruction, Compressed Sensing, Signal Modeling, Inverse Problems, Parameter Estimation, 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 approaches that combine: (1) the modeling and manipulation of physical imaging processes; (2) use of novel constrained signal and image models; (3) novel theory to characterize signal estimation frameworks; and (4) fast computational algorithms and hardware.
- 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
- 2010 IEEE ISBI Best Student Paper Award (first author)
- 2010 EEE EMBC Student Paper Competition First-Place Award (first author)
- 2011 University of Illinois at Urbana-Champaign M. E. Van Valkenburg Graduate Research Award
- 2014 NSF Career Award