B.S. Carnegie Mellon University, 2006M.S. Massachusetts Institute of Technology, 2008Ph.D. Massachusetts Institute of Technology, 2012Post-Doc University of Michigan, 2012-2014
"My research develops new ways to process image information, enhancing the quality and availability of state-of-the-art medical imaging."
Daniel S. Weller, Assistant Professor
Prior to joining UVA in 2014, Daniel Weller was a postdoctoral research fellow at the University of Michigan, in Ann Arbor, MI, where he worked on imaging research supported by a US National Institutes of Health Ruth L. Kirschstein National Research Service Award postdoctoral fellowship. He completed his SM and PhD in Electrical Engineering in 2008 and 2012 at MIT, in Cambridge, MA, preceded by completing his BS in Electrical and Computer Engineering in 2006 at Carnegie Mellon University in Pittsburgh, PA. He serves as an associate editor for the IEEE Transactions on Medical Imaging and is a member of the special interest group on Computational Imaging in the IEEE Signal Processing Society. He is also a team leader for UVA CHARGE, an NSF ADVANCE program aimed at recruiting and retaining women faculty in STEM and SBE fields. He is a member of IEEE, ISMRM, AHA, Eta Kappa Nu, and Tau Beta Pi.
Medical and Molecular Imaging
Signal and Image Processing
Optimization Models and Methods
"Comparison-based Image Quality Assessment for Selecting Image Restoration Parameters." IEEE Trans. Image Process., vol. 25, no. 11, pp. 5118-5130, November 2016. ABSHaoyi Liang and Weller, Daniel S.
"Undersampled Phase Retrieval with Outliers." IEEE Trans. Comput. Imaging, vol. 1, no. 4, pp. 247-258, December 2015. ABSWeller, Daniel S., Pnueli, Ayelet, Divon, Gilad, Radzyner, Ori, Eldar, Yonina C., and Fessler, Jeffrey A.
"Monte Carlo SURE-Based Parameter Selection for Parallel Magnetic Resonance Imaging Reconstruction." Magn. Reson. Med., vol. 71, no. 5, pp. 1760-1770, May 2014. ABSWeller, Daniel S., Ramani, Sathish, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A.
"Augmented Lagrangian with Variable Splitting for Faster Non-Cartesian L1-SPIRiT MR Image Reconstruction." IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 351-361, February 2014. ABSWeller, Daniel S., Ramani, Sathish, and Fessler, Jeffrey A.
"Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction." IEEE Trans. Med. Imag., vol. 32, no. 7, pp. 1325-1335, July 2013. ABSWeller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K.
ECE 4750/6750 Digital Signal ProcessingSpring Semester
ECE 2066 Science of Information: how the iPhone WorksFall Semester
Featured Grants & Projects
Jeffress Memorial Trust - Perceptually Aware Model Training for Image Reconstruction
This award by The Thomas F. and Kate Miller Jeffress Memorial Trust, Bank of America, Trustee, supports the development of new computational methods for reconstructing magnetic resonance images of the heart.
UVA Brain Institute Seed Grant - Automating Glia Neuroimaging
This project aims to produce a set of highly automatic computational tools for enhancing, processing, and analyzing microscope images of brain cells called microglia, to help neuroscientists study their function in the brain under different conditions.
NIH R21 EB022309 - Motion-Robust Methods for Rapid Pediatric MRI without Sedation
The overall goal of this project is to eliminate the need for sedation in pediatric magnetic resonance imaging (MRI), thus eliminating the risks of sedation to the child, reducing the length and cost of the exam, and enabling exams to be scheduled more quickly.