UTC Chair, Computer Science and Engineering
Director of Booth Engineering Center for Advanced Technologies (BECAT),
University of Connecticut, USA
Brief Speaker Bio :
Sanguthevar Rajasekaran received his M.E. degree in Automation from the Indian Institute of Science (Bangalore) in 1983, and his Ph.D. degree in Computer Science from Harvard University in 1988. Currently he is the Board of Trustees Distinguished Professor, UTC Chair Professor of Computer Science and Engineering, and the Director of Booth Engineering Center for Advanced Technologies (BECAT) at the University of Connecticut. Before joining UConn, he has served as a faculty member in the CISE Department of the University of Florida and in the CIS Department of the University of Pennsylvania. During 2000-2002 he was the Chief Scientist for Arcot Systems. His research interests include Big Data, Bioinformatics, Algorithms, Data Mining, Machine Learning, Randomized Computing, and HPC. He has published over 350 research articles in journals and conferences. He has co-authored two texts on algorithms and co-edited six books on algorithms and related topics. His research works have been supported by grants from such agencies as NSF, NIH, DARPA, and DHS (totaling around $20M). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the American Association for the Advancement of Science (AAAS). He is also an elected member of the Connecticut Academy of Science and Engineering.
Title of Talk
Algorithms for Big Data Analytics
We live in the midst of big data. Generation of data is no longer a bottleneck. Analysis of them and extracting useful information are indeed huge bottlenecks. Efficient techniques are needed to process these data. Society at large can benefit immensely from advances in this arena. For example, information extracted from biological data can result in gene identification, diagnosis for diseases, drug design, etc. Market-data information can be used for custom-designed catalogues for customers, supermarket shelving, and so on. Weather prediction and protecting the environment from pollution are possible with the analysis of atmospheric data.
In this talk we present some challenges existing in processing big data. We also provide an overview of some basic techniques. In particular, we will summarize various data processing and reduction techniques. In addition, we will briefly outline the role of machine learning in big data analytics.
Chandrajit Bajaj Computer Science, and Institute of Computational Engineering and Sciences Center for Computational Visualization, The University of Texas - Austinhttp://www.cs.utexas.edu/~bajaj
Brief Speaker Bio :
Chandrajit Bajaj is the director of the Center for Computational Visualization, in the Institute for Computational and Engineering Sciences (ICES) and a Professor of Computer Sciences at the University of Texas at Austin.
Bajaj holds the Computational Applied Mathematics Chair in Visualization.
He is also an affiliate faculty member of Mathematics, Computational Neuroscience and Electrical Engineering.
He is currently on the editorial boards for the International Journal of Computational Geometry and Applications, and the ACM Computing Surveys, and past editorial member of the SIAM Journal on Imaging Sciences.
He was awarded a distinguished alumnus award from the Indian Institute of Technology, Delhi, (IIT, Delhi).
He is also a Fellow of The American Association for the Advancement of Science (AAAS), Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronic Engineers (IEEE), and Fellow of the Society of Industrial and Applied Mathematics (SIAM).
Title of Talk
Unsupervised Super Resolution Hyperspectral Imaging
One achieves super resolution (SR) in imaging through computational enhancement of the input to yield output images which have improved resolvability of features. This is done by optimizing de-blurring and de-warping operators, and the appropriate leveraging of prior knowledge, and/or information from multiple similar images of the same scene or objects. Modern RGB digital cameras produce SR photographs of a scene by fusing a set of dynamically shifted acquired images of the scene. A related approach, using cross-modality imaging, can be used for producing SR hyperspectral (multi-spectral band) images. A low spatial resolution, hyperspectral image (LHI) and a high spatial resolution RGB image (Hrgb), can be optimally combined to produce a super resolution, hyperspectral image (SRHI). In this talk, I shall present new optimization methods based on low-rank or orthogonal tensor decompositions, and compare them with low-rank approximations of matricized representations of LHI and Hrgb. I shall also discuss variations stemming from different cross-modality imaging application (dynamic scene capture, facial and object recognition, cancer tissue histopathology) and where coupled optimization schemes for image registration and SR are necessary.