Daniel Y. Fu
Impact in
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- Complex Network Analysis Techniques
Papers in
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- Semantic Web and Ontologies 2
- AI-based Problem Solving and Planning 2
- Machine Learning and ELM 1
- Co-authors
- Kayvon Fatahalian (3 shared papers)Patrick Tan (2 shared papers)Yaroslav I. Molkov (2 shared papers)Haotian Zhang (1 shared paper)Michael Zhang (1 shared paper)Christopher Ré (3 shared papers)Maria V. Zaretskaia (1 shared paper)Daniel E. Rusyniak (1 shared paper)
- Journals
- PLoS ONE (2 papers)MDPI (MDPI AG) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesHong KongDominican Republic
In The Last Decade
Daniel Y. Fu
8 papers receiving 30 citations
Peers
Comparison fields: 5 of 29
- Endocrine and Autonomic Systems 3
- Statistical and Nonlinear Physics 5
- Information Systems 7
- Artificial Intelligence 10
- Communication 2
Countries citing papers authored by Daniel Y. Fu
This map shows the geographic impact of Daniel Y. Fu'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 Daniel Y. Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Y. Fu more than expected).
Fields of papers citing papers by Daniel Y. Fu
This network shows the impact of papers produced by Daniel Y. Fu. 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 Daniel Y. Fu. The network helps show where Daniel Y. Fu may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Y. Fu, 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 | 2021 | 11 | |
| 2 | 2015 | 6 | |
| 3 | 2014 | 4 | |
| 4 | Reasoning About Adversarial Intent in Asymmetric Situations | 2002 | 3 |
| 5 | 2022 | 3 | |
| 6 | Multi-Resolution Weak Supervision for Sequential Data | 2019 | 2 |
| 7 | A CBR Approach to Asymmetric Plan Detection | 2003 | 2 |
| 8 | Detecting Asymmetric Plans Using Case-Based Reasoning | 2003 | 1 |
| 9 | 2025 | 0 |
About Daniel Y. Fu
Daniel Y. Fu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Experimental and Cognitive Psychology and Molecular Biology, having authored 9 papers that have together received 32 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (2 papers), AI-based Problem Solving and Planning (2 papers), Software Engineering Research (2 papers), Evolutionary Psychology and Human Behavior (1 paper), Plant Molecular Biology Research (1 paper), COVID-19 diagnosis using AI (1 paper), Cell Image Analysis Techniques (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Endocrine and Autonomic Systems (3 citations), Statistical and Nonlinear Physics (5 citations), Information Systems (7 citations), Artificial Intelligence (10 citations) and Communication (2 citations). Daniel Y. Fu has collaborated with scholars based in United States, Hong Kong and Dominican Republic. Frequent co-authors include Kayvon Fatahalian, Patrick Tan, Yaroslav I. Molkov, Haotian Zhang, Michael Zhang, Christopher Ré, Maria V. Zaretskaia, Daniel E. Rusyniak, James Won‐Ki Hong and Jacob Ritchie. Their work appears in journals such as PLoS ONE, MDPI (MDPI AG) 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.