Bio

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

  • "Motion-compensated reconstruction of magnetic resonance images from undersampled data." Magnetic Resonance Imaging, vol. 55, pp. 36-45, January 2019. ABS Weller, Daniel S., Wang, Luonan, Mugler III, John P., and Meyer, Craig H.
  • "Content-Aware Compressive Magnetic Resonance Image Reconstruction." Magnetic Resonance Imaging, vol. 52, pp. 118-130, October 2018. ABS Weller, Daniel S., Salerno, Michael, and Meyer, Craig H.
  • "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.

Courses Taught

  • ECE 7776 Advanced Digital Signal Processing Fall Semester
  • ECE 4750/6750 Digital Signal Processing Spring Semester
  • ECE 2066 Science of Information: how the iPhone Works Fall Semester

Featured Grants & Projects

  • NSF 1759802 - ABI: Innovation: Analyzing Neuroglial Cell Dynamics in their Natural Environment with Video Microscopy


    The overall goal of this project is to produce modular, scalable, and open-source video processing and analysis software tools to advance neuroscientists' understanding of how neural and glial cells interaction with each other and with their environment.

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  • NIH R21 EB022309 - Motion-Robust Methods for Rapid Pediatric MRI without Sedation

    co-investigator


    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.

  • Fast and Automatic Reconstruction of High Frame-Rate Cardiac Magnetic Resonance

    UVA Center for Engineering in Medicine Seed Grant


    The overall goal of this project is to develop fast, scalable, and robust optimization algorithms for the reconstruction of cardiac magnetic resonance images from undersampled data.

  • 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.