Haesun Park


Haesun Park is a professor of Computational Science and Engineering at the Georgia Institute of Technology. She is an IEEE Fellow and Society for Industrial and Applied Mathematics Fellow. Park's main areas of research are Numerical Algorithms, Data Analysis, Visual Analytics and Parallel Computing. She has co-authored over 100 articles in peer-reviewed journals and conferences.

Education

Park graduated in 1981 from Seoul National University with a bachelor's degree in mathematics, and went on to graduate studies in computer science at Cornell University, earning a master's degree in 1985 and a Ph.D. in 1987 under the supervision of Franklin Tai-Cheung Luk.

Career

Park started her teaching career at the University of Minnesota as Assistant Professor and later became Associate Professor in the university. From 1998 to 2005, she was a professor in the department of computer science and engineering at the University of Minnesota. Park was a program director at the National Science Foundation from 2003 until 2005, before moving to Georgia Tech in 2005. She has also held an affiliation with the Korea Institute for Advanced Study since 2008. Park led the Foundations of Data and Visual Analytics center and received $3 million grant to support emerging field of massive data analysis and visual analytics. Currently, she serves on the Data Analytics Selection Committee of SDM/IBM and was a member of SIAM Fellow Selection Committee from 2015 to 2017.
In 2013 she became a fellow of the Society for Industrial and Applied Mathematics "for contributions to numerical analysis and the data sciences". Parks also sits on the editorial board of BIT Numerical Mathematics, and International Journal of Bioinformatics Research and Applications. Parks also plays leadership roles in: International Journal of Bioinformatics Research and Applications, SDM, IEEE Transactions on Pattern Analysis and Machine Intelligence, BIT Numerical Mathematics and others. She was granted the patent for Method and apparatus for high dimensional data visualization with three others.

Other work

*