Education Profile

  • Ph.D., Electrical and Computer Engineering, Georgia Institute of Technology, United States, 2008
  • M.S., Mathematics, Georgia Institute of Technology, United States, 2005
  • B.S., Applied Mathematics, Georgia Institute of Technology, United States, 2003

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications.

Education and Early Career

His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that aided in technology for video tracking, and medical image analysis.

During his time in KAUST, he directed the Computational Vision Lab, which developed novel mathematics and algorithms, as well as software for video and image understanding technology. His fundamental optimization algorithms have led to advancements in motion-based video segmentation and detection. His group also developed technology for seismic image analysis, electron microscopy images, and medical (MRI & CT) images. Prior to KAUST, he was a postdoctoral research associate with Prof. Stefano Soatto in the Vision Lab at the University of California, Los Angeles from 2008 to 2010.

Research Interest

His research lies in both theory and application of computer vision. He develop fundamental mathematical methods at the intersection of optimization, geometry, partial differential equations, and statistics for advancing computer vision.

Selected Publications

Sundaramoorthi, G., Yezzi, A. (2018). Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms, (Preprint).
Algarni, M., & Sundaramoorthi, G. (2018). SurfCut: Surfaces of Minimal Paths From Topological Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. doi:10.1109/tpami.2018.2811810
Lao, D., & Sundaramoorthi, G. (2017). Minimum Delay Moving Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.511
Khan, N., Hong, B.-W., Yezzi, A., & Sundaramoorthi, G. (2017). Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.188
Algarni, M., & Sundaramoorthi, G. (2016). SurfCut: Free-Boundary Surface Extraction. Lecture Notes in Computer Science, 171–186. doi:10.1007/978-3-319-46478-7_11
Sundaramoorthi, G., Hadwiger, M., Ben-Romdhane, M., Behzad, A. R., Madhavan, P., & Nunes, S. P. (2016). 3D Membrane Imaging and Porosity Visualization. Industrial & Engineering Chemistry Research, 55(12), 3689–3695. doi:10.1021/acs.iecr.6b00387
Yang, Y., Sundaramoorthi, G., & Soatto, S. (2015). Self-Occlusions and Disocclusions in Causal Video Object Segmentation. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.501
Yang, Y., Zhaojin Lu, & Sundaramoorthi, G. (2015). Coarse-to-fine region selection and matching. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2015.7299140