Colloquium Computer Science, Prof. Dr. João Luiz Dihl Comba (UFRGS, Brazil)

Title: Visual Data Analysis of Unstructured and Big Data

                           

Abstract:

Data acquisition has never been so broad, diverse, and accessible. Social networking, urban and government data, computer-based gadgets and many others generate immense amounts of raw and most often unstructured data. The combined use of data analysis algorithms with visualization techniques has been enabling the extraction of interesting insights over this data. In addition to the several challenges involved in this analysis, this data is often big and requires efficient algorithms and data structures. In this talk I will present several examples of research projects developed at UFRGS that describe visualization designs and analysis algorithms for di erent types of data. In addition, I will describe our recent efforts in designing efficient data structures and algorithms to support the visual analysis of big data.

Bio: Prof. Dr. João Luiz Dihl Comba is an Associate Professor at the  Instituto de Informática , UFRGS, Brazil since 2002, where he leads research activities, teaches undergraduate and graduate courses, and is advisor for Ph.D, M.Sc. and B.Sc. students. Dr. Comba has a Ph.D. in Computer Science from Stanford University, 2000, a M.Sc. in Systems Engineering and Computation from COPPE-UFRJ, 1991 and a B.Sc. in Computer Science from UFRGS, Universidade Federal do Rio Grande do Sul, 1987. His current research is on Visual Data Analysis, with emphasis on the following topics: visualization, visual analytics, geometric algorithms, spatial data structures, parallel computation and graphics hardware. He is co-chair of the Visualization Corner column of the journal Computing In Science & Engineering, and co-chair of tutorials for the 2016 IEEE Visualization conference.

Colloquium coordinators are Prof.dr. M. Aiello (e-mail : M.Aiello rug.nl ) and
Prof.dr. M. Biehl (e-mail: M.Biehl rug.nl )

http://www.rug.nl/research/jbi/news/colloquia/computerscience