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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Holly Rush
holly@cc.gatech.edu
Summary Sentence: Four Georgia Tech research hubs have launched a new "chalk & talk" brown bag lunch series on Big Data.
Full Summary: Four Georgia Tech research hubs have launched a new “chalk & talk” brown bag lunch series on Big Data. The weekly series, sponsored jointly by the Institute for Data & High Performance Computing (IDH), Institute for Materials (IMaT), Center for Data Analytics (CDA) and Center for High Performance Computing (HPC) will be held on most Thursdays during the Fall and Spring Semesters and feature a mix of topics, including those related to big data for materials and manufacturing, as well as other topics critical to the broader area of big data.
XDATA - Haesun ParkFour Georgia Tech research hubs have launched a new “chalk & talk” brown bag lunch series on Big Data. The weekly series, sponsored jointly by the Institute for Data & High Performance Computing (IDH), Institute for Materials (IMaT), Center for Data Analytics (CDA) and Center for High Performance Computing (HPC) will be held on most Thursdays during the Fall and Spring Semesters and feature a mix of topics, including those related to big data for materials and manufacturing, as well as other topics critical to the broader area of big data.
Date: January 23
Topic: "Interactive Visual Analytics for Text Data Analysis"
Presenter: Haesun Park
Abstract:
Many modern data sets can be represented in high dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from numerical linear algebra and optimization. Visual analytics approaches have contributed greatly to data understanding and analysis due to utilization of both automated algorithms and human’s quick visual perception and interaction. However, visual analytics targeting high dimensional large-scale data such as a document collection has been challenging due to low dimensional screen space with limited pixels to represent data.
We present some of the key foundational methods for supervised dimension reduction such as linear discriminant analysis (LDA), dimension reduction and clustering/topic discovery by nonnegative matrix factorization (NMF), and visual spatial alignment for effective fusion and comparisons by orthogonal Procrustes. We demonstrate how these methods can effectively support interactive visual analytic tasks that involve large-scale document data sets in two of the visual analytics systems, UTOPIAN for interactive topic discovery and VisIRR for visual document retrieval and recommendation.
Bio:
Prior to joining the College of Computing's School of Computational Science and Engineering faculty in 2005, Haesun Park was on the faculty at the University of Minnesota, Twin Cities, from 1987 to 2005, and from 2003 to 2005, she also served as a program director for the Computing and Communication Foundations Division at the National Science Foundation in Arlington, Va. She is the director of the NSF/DHS Foundations of Data and Visual Analytics (FODAVA-Lead) initiative and executive director of the Center for Data Analytics (CDA) at Georgia Tech.
Park's current research interests include numerical algorithms, pattern recognition, bioinformatics, information retrieval, and data mining. She has published more than 100 research papers in these areas and has served on numerous conference committees and journal editorial boards. Currently, she is on the editorial board of BIT Numerical Mathematics, SIAM Journal on Matrix Analysis and Applications, and the International Journal of Bioinformatics Research and Applications. In 2008 and 2009, Park served as a conference co-chair for the SIAM International Conference on Data Mining, and in 2013, she was elected as a SIAM Fellow.
