World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
15997
World Ranking
3392
National Ranking
1644

Mathematics

D-Index
57
Citations
15261
World Ranking
679
National Ranking
344

Research.com Recognitions

  • 2020 - ACM Fellow For contributions to numerical algorithms, data analytics, and leadership in computational science and engineering
  • 2017 - IEEE Fellow For contributions to large-scale data and visual analytics
  • 2013 - SIAM Fellow For contributions to numerical analysis and the data sciences.

Overview

Haesun Park is affiliated with the Georgia Institute of Technology in the United States. Their research spans across the fields of Engineering and Materials Science, with a focus on subfields such as Electrical and Electronic Engineering, Materials Chemistry, Pharmaceutical Science, Electronic, Optical and Magnetic Materials, and Inorganic Chemistry.

The scientist's work covers a range of topics, including:

  • Advancements in Battery Materials
  • Advanced Battery Materials and Technologies
  • Advanced Drug Delivery Systems
  • Supercapacitor Materials and Fabrication
  • Inorganic Chemistry and Materials
  • Advanced Battery Technologies Research
  • Advancements in Solid Oxide Fuel Cells

Haesun Park has published recent papers such as:

  • "Evolution of drug delivery systems: From 1950 to 2020 and beyond," 2021, Journal of Controlled Release
  • "Theoretical Design of Lithium Chloride Superionic Conductors for All-Solid-State High-Voltage Lithium-Ion Batteries," 2020, ACS Applied Materials & Interfaces (authored by Dongsu Park)
  • "High-Voltage Phosphate Cathodes for Rechargeable Ca-Ion Batteries," 2020, ACS Energy Letters (authored by Sang-Hyeon Kim)
  • "Formulation composition, manufacturing process, and characterization of poly(lactide-co-glycolide) microparticles," 2020, Journal of Controlled Release (authored by Kinam Park)
  • "Potential Roles of the Glass Transition Temperature of PLGA Microparticles in Drug Release Kinetics," 2020, Molecular Pharmaceutics (authored by Kinam Park)

Frequent co-authors include:

  • Peter Zapol
  • Robert F. Klie
  • John T. Vaughey
  • Kinam Park
  • Saul H. Lapidus

The scientist's frequent publication venues are:

  • ECS Meeting Abstracts
  • Journal of Controlled Release
  • Chemistry of Materials
  • ACS Energy Letters
  • Molecular Pharmaceutics

Haesun Park has received several awards including:

  • ACM Fellow (2020) for contributions to numerical algorithms, data analytics, and leadership in computational science and engineering
  • IEEE Fellow (2017) for contributions to large-scale data and visual analytics
  • SIAM Fellow (2013) for contributions to numerical analysis and the data sciences

Best Publications

  • Orthogonal nonnegative matrix t-factorizations for clustering

    Chris Ding;Tao Li;Wei Peng;Haesun Park

  • Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

    Hyunsoo Kim;Haesun Park

  • Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method

    Hyunsoo Kim;Haesun Park

  • Missing value estimation for DNA microarray gene expression data: local least squares imputation

    Hyunsoo Kim;Gene H. Golub;Haesun Park

  • Symmetric Nonnegative Matrix Factorization for Graph Clustering.

    Da Kuang;Chris Ding;Haesun Park

  • Generalizing discriminant analysis using the generalized singular value decomposition

    P. Howland;H. Park

  • Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework

    Jingu Kim;Yunlong He;Haesun Park

  • Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons

    Jingu Kim;Haesun Park

  • Dimension Reduction in Text Classification with Support Vector Machines

    Hyunsoo Kim;Peg Howland;Haesun Park

  • UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

    Jaegul Choo;Changhyun Lee;Chandan K. Reddy;Haesun Park

  • An optimization criterion for generalized discriminant analysis on undersampled problems

    Jieping Ye;R. Janardan;C.H. Park;H. Park

  • Magnetic field imaging with NV ensembles

    L. M. Pham;D. Le Sage;P. L. Stanwix;T. K. Yeung

  • Protein secondary structure prediction based on an improved support vector machines approach.

    Hyunsoo Kim;Haesun Park

  • Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons

    Jingu Kim;Haesun Park

  • Total Least Norm Formulation and Solution for Structured Problems

    J. Ben Rosen;Haesun Park;John Glick

  • Sparse Nonnegative Matrix Factorization for Clustering

    Jingu Kim;Haesun Park

  • A new optimization criterion for generalized discriminant analysis on undersampled problems

    J. Ye;Ravi Janardan;C.H. Park;H. Park

  • A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering

    Yunjae Jung;Haesun Park;Ding-Zhu Du;Barry L. Drake

  • Structure Preserving Dimension Reduction for Clustered Text Data Based on the Generalized Singular Value Decomposition

    Peg Howland;Moongu Jeon;Haesun Park

  • SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering

    Da Kuang;Sangwoon Yun;Haesun Park

  • SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping

    Ioakeim Perros;Evangelos E. Papalexakis;Haesun Park;Richard Vuduc

Frequent Co-Authors

Jaegul Choo
Jaegul Choo Korea Advanced Institute of Science and Technology
John Stasko
John Stasko Georgia Institute of Technology
Lars Eldén
Lars Eldén Linköping University
Chandan K. Reddy
Chandan K. Reddy Virginia Tech
Jieping Ye
Jieping Ye Alibaba Group (China)
Moongu Jeon
Moongu Jeon Gwangju Institute of Science and Technology
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen
Alex Endert
Alex Endert Georgia Institute of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Mathematics in the USA opens doors to a variety of related online degrees and career paths. For students interested in advancing their careers with a business focus, exploring can you transfer mba programs is an excellent way to boost qualifications while managing time efficiently.

Data-driven fields like data analytics are a natural extension for math graduates. Pursuing a masters data analytics degree online equips students with essential skills in interpreting complex data and making informed decisions in various industries.

For those seeking flexibility, some programs highlight the easiest mba specialization options, allowing students to find a manageable path that suits their learning style and career goals. Similarly, researching the easiest mba online programs can help design an educational journey that balances rigor with accessibility.

These related degrees not only complement a math foundation but also broaden professional opportunities in technology, finance, and management.

Best Scientists Citing Haesun Park

Trending Scientists

Recently Published Articles