Prof. Sanjay Ranka

Professor
Fellow IEEE
Fellow AAAS

University of Florida, USA
    

Sanjay Ranka is a Professor in the Department of Computer Science. His current research interests are data mining, informatics and grid computing for data intensive applications in High Energy Physics, BioTerrorism and BioMedical Computing. Most recently he was the Chief Technology Officer at Paramark where he developed real-time optimization software for optimizing marketing campaigns. Sanjay has also held positions as a tenured faculty positions at Syracuse University and as a researcher/visitor at IBM T.J. Watson Research Labs and Hitachi America Limited. Sanjay earned his Ph.D. (Computer Science) from the University of Minnesota in 1988 and a B. Tech. in Computer Science from IIT, Kanpur, India in 1985. He has coauthored two books: Elements of Neural Networks (MIT Press) and Hypercube Algorithms (Springer Verlag), 50+ journal articles and 80+ refereed conference articles. He serves on the editorial board of the Journal of Parallel and Distributed Computing and was a past member of the Parallel Compiler Runtime Consortium and the Message Passing Initiative Standards Committee. He was one of the main architects of the Syracuse Fortran 90D/HPF compiler. He is a fellow of the IEEE and AAAS, advisory board member of IEEE Technical Committee on Parallel Processing and a member of IFIP Committee on System Modeling and Optimization.

Title: An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

Abstract: In this talk, We present a novel framework for semi-supervised labeling of regions in remote sensing image datasets. Our approach works by decomposing the image into irregular patches or superpixels and derives novel features based on intensity histograms, geometry, corner density, and scale of tessellation. Our classification pipeline uses either k-nearest neighbors or SVM to obtain a preliminary classification which is then refined using Laplacian propagation algorithm. Our approach is easily parallelizable and fast despite the high volume of data involved. Results are presented which showcase the accuracy as well as different stages of our pipeline.

https://www.cise.ufl.edu/people/faculty/ranka/