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.

Research Interests

  • Medical and Molecular Imaging
  • Signal and Image Processing
  • Optimization Models and Methods

Selected Publications

  • "Comparison-based Image Quality Assessment for Selecting Image Restoration Parameters." IEEE Trans. Image Process., vol. 25, no. 11, pp. 5118-5130, November 2016. ABS Haoyi Liang and Weller, Daniel S.
  • "Undersampled Phase Retrieval with Outliers." IEEE Trans. Comput. Imaging, vol. 1, no. 4, pp. 247-258, December 2015. ABS Weller, 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. ABS Weller, 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. ABS Weller, 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. ABS Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K.

Courses Taught

  • ECE 4750/6750 Digital Signal Processing Spring Semester
  • ECE 2066 Science of Information: how the iPhone Works Fall 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.