Haesun Park

Haesun Park

Professor and Associate Chair for Graduate Studies
Director of NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics)

Faculty Bio: http://www.cc.gatech.edu/~hpark

High Performance Computing Area

Algorithms and Applications

Research Areas

Numerical algorithms, pattern recognition, bioinformatics, information retrieval, scientific computing, parallel computing, text analysis, and data mining

Research Summary

Enormous amounts of data are being generated in healthcare, computational biology, homeland security, and other areas, but analyzing these massive and unstructured data sets has proven difficult. Haesun Park specializes in using numerical linear algebra and optimization techniques to develop computer-based algorithms that dramatically reduce the dimension and number of data points in massive data sets. Dimension reduction is essential for efficient processing of high-dimension data sets while removing the noise in the data. Park and her research team are especially interested in developing methods for dimension reduction that exploit prior knowledge in the data sets such as clustered structures and non-negativity. This process is important because it leads to more accurate classification and prediction results.