Sungsoo Ahn
Impact in
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- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Topic Modeling
- Explainable Artificial Intelligence (XAI)
- Privacy-Preserving Technologies in Data
Papers in
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- Imbalanced Data Classification Techniques 1
- Machine Learning and Algorithms 1
- Natural Language Processing Techniques 1
- Bayesian Modeling and Causal Inference 1
- Gaussian Processes and Bayesian Inference 1
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- Chemical Synthesis and Analysis 1
- Co-authors
- Jinwoo Shin (3 shared papers)Jaeho Lee (1 shared paper)Junsu Kim (1 shared paper)Michael Chertkov (1 shared paper)Jaehyung Kim (1 shared paper)
- Journals
- Journal of Statistical Mechanics Theory and Experiment (1 paper)Neural Information Processing Systems (1 paper)Open Access System for Information Sharing (Pohang University of Science and Technology) (1 paper)
- Partner nations
- South KoreaUnited States
In The Last Decade
Sungsoo Ahn
3 papers receiving 73 citations
Peers
Comparison fields: 5 of 23
- Health Informatics 3
- Artificial Intelligence 61
- Computer Vision and Pattern Recognition 31
- Safety Research 7
- Radiology, Nuclear Medicine and Imaging 4
Countries citing papers authored by Sungsoo Ahn
This map shows the geographic impact of Sungsoo Ahn'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 Sungsoo Ahn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungsoo Ahn more than expected).
Fields of papers citing papers by Sungsoo Ahn
This network shows the impact of papers produced by Sungsoo Ahn. 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 Sungsoo Ahn. The network helps show where Sungsoo Ahn may publish in the future.
Co-authors
The 5 scholars most cited alongside Sungsoo Ahn, 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 | Learning from Failure: De-biasing Classifier from Biased Classifier | 2020 | 72 |
| 2 | Guiding Deep Molecular Optimization with Genetic Exploration | 2020 | 3 |
| 3 | 2019 | 1 | |
| 4 | 2025 | 0 |
About Sungsoo Ahn
Sungsoo Ahn is a scholar working on Artificial Intelligence, Molecular Biology, Computational Theory and Mathematics, Materials Chemistry and Infectious Diseases, having authored 4 papers that have together received 76 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (1 paper), Machine Learning and Algorithms (1 paper), Computational Drug Discovery Methods (1 paper), Natural Language Processing Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Machine Learning in Materials Science (1 paper) and Chemical Synthesis and Analysis (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (61 citations), Computer Vision and Pattern Recognition (31 citations), Safety Research (7 citations) and Radiology, Nuclear Medicine and Imaging (4 citations). Sungsoo Ahn has collaborated with scholars based in South Korea and United States. Frequent co-authors include Jinwoo Shin, Jaeho Lee, Junsu Kim, Michael Chertkov and Jaehyung Kim. Their work appears in journals such as Journal of Statistical Mechanics Theory and Experiment, Neural Information Processing Systems and Open Access System for Information Sharing (Pohang University of Science and Technology).
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.