MR5, Room 1115
​MR5, Room 1207
415 Lane Road (MR5 Building)
Charlottesville, VA 22908
Google Scholar Rohde Lab


Dr. Rohde develops computational predictive models using machine learning and signal and image processing with applications in pathology, radiology, systems biology, and mobile sensing. He earned B.S. degrees in physics and mathematics in 1999, and the M.S. degree in Electrical Engineering in 2001 from Vanderbilt University. He received a doctorate in applied mathematics and scientific computation in 2005 from the University of Maryland. He is Professor of Biomedical Engineering and Electrical and Computer Engineering at the University of Virginia.

Research in the Imaging and Data Science Lab aims to contribute ideas in support of biomedical imaging, mobile, and remote sensing applications. We specialize on objective and quantitative modeling of data from imaging and other types of sensors by incorporating knowledge from multiple disciplines including applied mathematics, signal processing, machine learning and statistics.


B.S. Physics and Mathematics, Vanderbilt University, 1999

M.S. Electrical Engineering, Vanderbilt University, 2001

Ph.D. Applied Mathematics and Scientific Computation, University of Maryland, 2005

We teach and develop techniques that allow us to make better sense of signals, images, and digital data in general.

Gustavo Kunde Rohde Professor

Research Interests

Medical and Molecular Imaging
Signal and Image Processing
Biomedical Data Sciences
Computer Graphics and Vision
Biomedical Data Sciences
Machine Learning

Selected Publications

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning, Proceedings Of The National Academy Of Sciences 117 (40), 24709-24719 S. Kundu, B.G. Ashinsky, M. Bouhrara, E.B. Dam, S. Demehri, M. Shifat-E-Rabbi, R.G. Spencer, K.L. Urish, and G.K. Rohde
Parametric signal estimation using the cumulative distribution transform, IEEE Transactions On Signal Processing (2020) A.H.M. Rubaiyat, K. Hallam, J. Nichols, M. Hutchinson, S. Li, G.K. Rohde
Cell image classification: a comparative overview, Cytometry Part A 97 (4), 347-362 (2020) M. Shifat‐E‐Rabbi, X. Yin, C.E. Fitzgerald, G.K. Rohde
Generalized sliced wasserstein distances, Advances In Neural Information Processing Systems 261-272 (2019) S. Kolouri, K. Nadjahi, U. Simsekli, R. Badeau, G.K. Rohde
Methods to label, image, and analyze the complex structural architectures of microvascular networks, Microcirculation 26 (5), E12520 (2019) B.A. Corliss, C. Mathews, R. Doty, G.K. Rohde, S.M. Peirce
The cumulative distribution transform and linear pattern classification, Applied and Computational Harmonic Analysis 45 (3), 616-641 (2018) S.R. Park, S. Kolouri, S. Kundu, G.K. Rohde
Optimal mass transport: Signal processing and machine-learning applications, IEEE Signal Processing Magazine 34 (4), 43-59 (2017) S. Kolouri, S.R. Park, M. Thorpe, D. Slepcev, G.K. Rohde