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 Computational Imaging Technical Committee in the IEEE Signal Processing Society. He also served as 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, Eta Kappa Nu, and Tau Beta Pi.
Medical and Molecular Imaging
Signal and Image Processing
Optimization Models and Methods
"Content-Aware Enhancement of Images with Filamentous Structures." IEEE Trans. Image Process., vol. 28, no. 7, pp. 3451-3461, July 2019. ABSJeelani, Haris, Haoyi Liang, Acton, Scott T., and Weller, Daniel S.
"Structure-based Intensity Propagation for 3D Brain Reconstruction with Multilayer Section Microscopy." IEEE Trans. Med. Imaging, vol. 38, no. 5, pp. 1106-1115, May 2019. ABSHaoyi Liang, Dabrowska, Natalia, Kapur, Jaideep, and Weller, Daniel S.
"Motion-compensated reconstruction of magnetic resonance images from undersampled data." Magnetic Resonance Imaging, vol. 55, pp. 36-45, January 2019. ABSWeller, 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. ABSWeller, 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. ABSHaoyi Liang and Weller, Daniel S.
APMA 2501 Mathematics of InformationSpring Semester
ECE 6711 Probability and Stochastic ProcessesFall Semester
ECE 4750/6750 Digital Signal ProcessingSpring Semester
ECE 7776 Advanced Digital Signal ProcessingFall Semester
Featured Grants & Projects
NIH R56 EB028254 - Generalizing Deep Learning Reconstruction for Free-Breathing and Quantitative MRI
The major goal of this project is to develop machine-learning methods for high-resolution, free-breathing quantitative MRI and apply it to T1 mapping of the myocardium to identify diffuse and focal scar and help differentiate among left ventricular hypertrophies.
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.