Sanghyun Park
- Signal Processing top 1%
- Data Management and Algorithms 18
- Molecular Biology top 5%
- Bioinformatics and Genomic Networks 27
- Gene expression and cancer classification 22
- Machine Learning in Bioinformatics 19
- Protein Structure and Dynamics 17
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- Advanced Data Storage Technologies 27
- Caching and Content Delivery 22
- Artificial Intelligence top 2%
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- Cloud Computing and Resource Management 17
Sanghyun Park
218 papers receiving 4.7k citations
Peers
Comparison fields: 5 of 204
- Signal Processing 533
- Molecular Biology 2.5k
- Computer Networks and Communications 586
- Artificial Intelligence 746
- Computational Theory and Mathematics 346
Countries citing papers authored by Sanghyun Park
This map shows the geographic impact of Sanghyun Park's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sanghyun Park with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanghyun Park more than expected).
Fields of papers citing papers by Sanghyun Park
This network shows the impact of papers produced by Sanghyun Park. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sanghyun Park. The network helps show where Sanghyun Park may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sanghyun Park, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 3 | |
| 5 | 2016 | 0 | |
| 6 | 2015 | 47 | |
| 7 | 2015 | 1 | |
| 8 | Sunk cost effect in the Korea basketball league. | 2015 | 1 |
| 9 | 2014 | 11 | |
| 10 | 2014 | 14 | |
| 11 | 2013 | 15 | |
| 12 | A survey of sequence alignment algorithms for next-generation sequencing read | 2012 | 0 |
| 13 | Multi-class Classification of Database Workloads using PCA-SVM Classifier | 2011 | 1 |
| 14 | Game Behavior Pattern Modeling for Bots(Auto Program) detection | 2009 | 0 |
| 15 | Recurrent Brucellar Meningoencephalitis | 2008 | 1 |
| 16 | Towards Efficient Searching on the Secondary Structure of Protein Sequences | 2007 | 1 |
| 17 | Building a Classifier for Integrated Microarray Datasets through Two-Stage Approach | 2007 | 1 |
| 18 | Induction of Apoptosis by Curcuma aromatica on Lung Cancer Cells(A549), Cervical Cancer Cells(HeLa), Glioma Cancer Cells(A172) and Prostate Cancer Cells(PC3) | 2006 | 1 |
| 19 | Real-Time Rate Control with Token Bucket for Low Bit Rate Video | 2006 | 0 |
| 20 | On space management in a dynamic edge data cache. | 2002 | 9 |
About Sanghyun Park
Sanghyun Park is a scholar working on Computer Networks and Communications, Signal Processing and Hardware and Architecture, having authored 245 papers that have together received 4.9k indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (27 papers), Bioinformatics and Genomic Networks (27 papers), Caching and Content Delivery (22 papers), Gene expression and cancer classification (22 papers), Machine Learning in Bioinformatics (19 papers), Data Management and Algorithms (18 papers), Protein Structure and Dynamics (17 papers) and Cloud Computing and Resource Management (17 papers). The work is most often cited by research in Signal Processing (533 citations), Molecular Biology (2.5k citations) and Computer Networks and Communications (586 citations). Sanghyun Park has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Ali Zarrinpar, Wendell A. Lim, Klaus Schulten, Chihyun Park, Vijay S. Pande, Sang‐Wook Kim, Wesley W. Chu, Morten Ø. Jensen, Emad Tajkhorshid and Jaegyoon Ahn. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.