Sungsoo Ahn
Impact in
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- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Explainable Artificial Intelligence (XAI)
- Topic Modeling
- Privacy-Preserving Technologies in Data
Papers in
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- Machine Learning and Data Classification 1
- Natural Language Processing Techniques 1
- Machine Learning and Algorithms 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)Open Access System for Information Sharing (Pohang University of Science and Technology) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- South KoreaUnited States
In The Last Decade
Sungsoo Ahn
3 papers receiving 71 citations
Peers
Comparison fields: 5 of 21
- Health Informatics 3
- Artificial Intelligence 60
- Computer Vision and Pattern Recognition 29
- Safety Research 7
- Computational Theory and Mathematics 3
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 | 70 |
| 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 74 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), Natural Language Processing Techniques (1 paper), Computational Drug Discovery Methods (1 paper), Machine Learning and Algorithms (1 paper), Bayesian Modeling and Causal Inference (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Chemical Synthesis and Analysis (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (60 citations), Computer Vision and Pattern Recognition (29 citations), Safety Research (7 citations) and Computational Theory and Mathematics (3 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, Open Access System for Information Sharing (Pohang University of Science and Technology) and Neural Information Processing Systems.
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.