General Information
Details
Professor Park’s teaching is focused on numerical analysis, matrix computation, and data analytics at the undergraduate and graduate levels. Her goal in teaching is to motivate the students to appreciate and understand fundamental methods and at the same time learn their value in real-life applications with practical impacts.
Professor Park’s research focuses on the development of effective and efficient methods for data analytics based on the foundation of numerical linear algebra, for the tasks including clustering, embedding, information fusion, and community detection. Her recent work also involves developing high performance scalable data analytics algorithms and open-source software for massive data sets, and visual analytics that achieves true integration of algorithms, visualization, and interactions.
Selected Recent Distinctions and Awards
Stephen Fleming Endowed Chair Professorship, College of Computing, Georgia of Tech, June 2025
Best Paper Award, IEEE International Conference on Big Data, Sorrento, Italy, December 2023
ACM Fellow (Association for Computing Machinery), 2020
IEEE Vis Conference VAST (Visual Analytics Science and Technology) 10 Year Test-of-Time Award, October 2020
Regents' Professor (the appointment was renewed twice), Georgia Tech, June 2019
IEEE Fellow (Institute of Electrical and Electronics Engineers), 2017
Best Poster Award, IEEE Conference on Visual Analytics Science and Technology, Chicago, IL, 2014
SIAM Fellow (Society for Industrial and Applied Mathematics), 2013
Inaugural Member of IEEE Visualization Pioneers Group, 2013
Selected Recent Publications
D. Choi and H. Park,
Effective co-embedding of multi-type data via integrated symmetric nonnegative matrix factorization,
Linear Algebra and its Applications, to appear.
K. Hayashi, S. Aksoy, G. Ballard, H. Park,
Randomized algorithms for symmetric nonnegative matrix factorization,
SIAM Journal on Matrix Analysis and Applications,
46-1:584-625, 2025.
B. Cobb, R. Velasquez, R. Vuduc, and H. Park,
Clustering and topic discovery of multiway data via Joint-NCMTF,
IEEE International Conference on Big Data (IEEE BigData24),
Washington DC, December 15-18, 2024
S. Eswar, K. Harashi, B. Cobb, R. Kannan, G. Ballard, R. Vuduc, and H. Park,
On rank selection for nonnegative matrix factorization,
IEEE International Conference on Big Data (IEEE BigData24),
Washington DC, December 15-18, 2024
D. Choi, A. Xiang, O. Ozturk, D. Shrestha, B. Drake, H. Haidarian, F. Javed, and H. Park,
WellFactor: Patient profiling using integrative embedding of healthcare data,
IEEE International Conference on Big Data (IEEE BigData23),
Best Paper Award Winner, Sorrento, Italy, December 15-18, 2023
S. Eswar, B. Cobb, K. Hayashi, R. Kannan, G. Ballard, R. Vuduc, and H. Park,
Distributed-memory Parallel JointNMF,
ACM International Conference on Supercomputing (ACM ICS23),
Orlando, FL, June 21-23, 2023
D. Choi, B. Drake, and H. Park,
Co-embedding multi-type data for information fusion and visual analytics,
IEEE International Conference on Information Fusion,
Charleston, SC, June 27-30, 2023
K. Hayashi, S. Aksoy, and H. Park,
Skew-symmetric Adjacency Matrices for Clustering Directed Graphs,
IEEE International Conference on Big Data (IEEE BigData22),
Osaka, Japan, December 17-20, 2022
S. Eswar, R. Kannan, R. Vuduc, and H. Park,
ORCA: Outlier detection and Robust Clustering for Attributed graphs,
Journal of Global Optimization, 81-4:967-989, 2021
D. Chu, W. Shi, S. Eswar, and H. Park,
An alternating rank-k nonnegative least squares framework (ARkNLS)
for nonnegative matrix factorization,
SIAM Journal on Matrix Analysis and Applications,
42:4, 2021
S. Eswar, K. Hayashi, G. Ballard, R. Kannan, M. Matheson, and H. Park,
PLANC: Parallel Low Rank Approximation with Non-negativity Constraints,
ACM Transactions on Mathematical Software,
47-3:1-37, 2021
H. Kim, B. Drake, A. Endert, and H. Park,
ArchiText: Interactive hierarchical topic modeling,
IEEE Transactions on Visualization and Computer Graphics,
27-9:3644-3655, 2021