PhD Applied Mathematics and Scientific Computation University of Maryland 2005MS Electrical Engineering Vanderbilt University 2001BS Physics and Mathematics Vanderbilt University 1999
"We teach and develop techniques that allow us to make better sense of signals, images, and digital data in general."
Gustavo Kunde Rohde, Professor
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.
A Leading Expert in the Development of diagnostic AI Applications for Tissue Pathology
"How do you establish the science of characterising biological heterogeneity, within a tissue? That step is, in my view, the best way for us to build sufficient knowledge upon which to derive algorithms which are robust enough and competent enough to address the right questions."
Published in Proceedings of the National Academy of Sciences and featured by CBS News, NIH, MSN, and more
The research team investigated whether machine learning applied to MRI images of knee cartilage can point to early signs of osteoarthritis and predict who will develop the disease. The team used a customized machine learning technique invented in Rohde’s lab called transport-based morphometry, an approach that quantifies information about the shape and texture of biological forms seamlessly from image data.
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning | S Kundu, BG Ashinsky, M Bouhrara, EB Dam, S Demehri, ...GK Rohde ABSProceedings of the National Academy of Sciences 117 (40), 24709-24719
Parametric signal estimation using the cumulative distribution transform | AHM Rubaiyat, K Hallam, J Nichols, M Hutchinson, S Li, G Rohde ABSIEEE Transactions on Signal Processing (2020)
Cell image classification: a comparative overview | M Shifat‐E‐Rabbi, X Yin, CE Fitzgerald, GK Rohde ABSCytometry Part A 97 (4), 347-362 (2020)
Generalized sliced wasserstein distances | S Kolouri, K Nadjahi, U Simsekli, R Badeau, G Rohde ABSAdvances in Neural Information Processing Systems 261-272 (2019)
Methods to label, image, and analyze the complex structural architectures of microvascular networks | BA Corliss, C Mathews, R Doty, G Rohde, SM Peirce ABSMicrocirculation 26 (5), e12520 (2019)
The cumulative distribution transform and linear pattern classification SR Park, S Kolouri, S Kundu, GK Rohde ABSApplied and Computational Harmonic Analysis 45 (3), 616-641 (2018)
Optimal mass transport: Signal processing and machine-learning applications | S Kolouri, SR Park, M Thorpe, D Slepcev, GK Rohde ABSIEEE signal processing magazine 34 (4), 43-59 (2017)