Tze-Yun Leong

1.8k total citations
88 papers, 855 citations indexed

About

Tze-Yun Leong is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Tze-Yun Leong has authored 88 papers receiving a total of 855 indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Artificial Intelligence, 24 papers in Molecular Biology and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Tze-Yun Leong's work include Bayesian Modeling and Causal Inference (18 papers), Biomedical Text Mining and Ontologies (18 papers) and AI-based Problem Solving and Planning (11 papers). Tze-Yun Leong is often cited by papers focused on Bayesian Modeling and Causal Inference (18 papers), Biomedical Text Mining and Ontologies (18 papers) and AI-based Problem Solving and Planning (11 papers). Tze-Yun Leong collaborates with scholars based in Singapore, United States and Sweden. Tze-Yun Leong's co-authors include Manish Sarkar, Tomi Silander, Boon Chuan Pang, Manash Sarkar, Zhuoru Li, Guoliang Li, C. C. Tchoyoson Lim, Klaus A. Kuhn, Arjun K. Manrai and Kun‐Hsing Yu and has published in prestigious journals such as New England Journal of Medicine, Artificial Intelligence and IEEE Transactions on Cybernetics.

In The Last Decade

Tze-Yun Leong

75 papers receiving 794 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tze-Yun Leong Singapore 18 360 160 120 99 76 88 855
Bo Jin China 19 490 1.4× 132 0.8× 139 1.2× 105 1.1× 22 0.3× 86 1.2k
Honghan Wu United Kingdom 18 528 1.5× 263 1.6× 159 1.3× 51 0.5× 125 1.6× 77 1.1k
Daby Sow United States 14 235 0.7× 52 0.3× 102 0.8× 191 1.9× 42 0.6× 58 693
Matloob Khushi Australia 20 737 2.0× 116 0.7× 197 1.6× 78 0.8× 69 0.9× 56 1.5k
Fabian Praßer Germany 17 547 1.5× 65 0.4× 90 0.8× 21 0.2× 63 0.8× 80 999
Pari Delir Haghighi Australia 18 217 0.6× 41 0.3× 39 0.3× 189 1.9× 85 1.1× 66 981
Nikita Jain India 17 424 1.2× 161 1.0× 101 0.8× 267 2.7× 34 0.4× 85 1.3k
Ernesto Iadanza Italy 17 133 0.4× 39 0.2× 163 1.4× 71 0.7× 44 0.6× 77 878
Ivan Rozman Slovenia 14 361 1.0× 41 0.3× 111 0.9× 45 0.5× 47 0.6× 68 1.2k
Saifuddin Mahmud Bangladesh 19 173 0.5× 215 1.3× 50 0.4× 166 1.7× 30 0.4× 62 1.0k

Countries citing papers authored by Tze-Yun Leong

Since Specialization
Citations

This map shows the geographic impact of Tze-Yun Leong'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 Tze-Yun Leong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tze-Yun Leong more than expected).

Fields of papers citing papers by Tze-Yun Leong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tze-Yun Leong. 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 Tze-Yun Leong. The network helps show where Tze-Yun Leong may publish in the future.

Co-authorship network of co-authors of Tze-Yun Leong

This figure shows the co-authorship network connecting the top 25 collaborators of Tze-Yun Leong. A scholar is included among the top collaborators of Tze-Yun Leong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tze-Yun Leong. Tze-Yun Leong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wei, Pengfei, Yiping Ke, Xinghua Qu, & Tze-Yun Leong. (2021). Subdomain Adaptation With Manifolds Discrepancy Alignment. IEEE Transactions on Cybernetics. 52(11). 11698–11708. 19 indexed citations
2.
Wei, Pengfei, Xinghua Qu, Yiping Ke, Tze-Yun Leong, & Yew-Soon Ong. (2020). Adaptive Knowledge Transfer based on Transfer Neural Kernel Network. Adaptive Agents and Multi-Agents Systems. 1485–1493. 1 indexed citations
3.
Narayan, Akshay & Tze-Yun Leong. (2019). Effects of Task Similarity on Policy Transfer with Selective Exploration in Reinforcement Learning. Adaptive Agents and Multi-Agents Systems. 2132–2134. 1 indexed citations
4.
Leong, Tze-Yun. (2017). Toward a collaborative AI framework for assistive dementia care. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 547. 1 indexed citations
5.
Haux, Reinhold, Casimir A. Kulikowski, Suzanne Bakken, et al.. (2017). Research Strategies for Biomedical and Health Informatics. Methods of Information in Medicine. 56(S 01). e1–e10. 12 indexed citations
6.
Li, Zhuoru, Akshay Narayan, & Tze-Yun Leong. (2017). An Efficient Approach to Model-Based Hierarchical Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 10 indexed citations
7.
Lehmann, Christoph U., Jan Talmon, Dominik Aronsky, et al.. (2013). Writing for Publication in Biomedical Informatics Journals. Singapore Management University Institutional Knowledge (InK) (Singapore Management University). 1253. 2 indexed citations
8.
Li, Zhuoru, et al.. (2013). Online Feature Selection for Model-based Reinforcement Learning. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1. 498–506. 26 indexed citations
9.
Silander, Tomi, et al.. (2012). Transferring Expectations in Model-based Reinforcement Learning. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 25. 2555–2563. 10 indexed citations
10.
Silander, Tomi, et al.. (2011). A generative model based approach to retrieving ischemic stroke images.. PubMed Central. 2 indexed citations
11.
Brochhausen, Mathias, Anita Burgun, Werner Ceusters, et al.. (2011). Discussion of “Biomedical Ontologies: Toward Scientific Debate”. Methods of Information in Medicine. 50(3). 217–236. 16 indexed citations
12.
Gefeller, Olaf, Dominik Aronsky, Tze-Yun Leong, et al.. (2011). The Birth and Evolution of a Discipline Devoted to Information in Biomedicine and Health Care. Methods of Information in Medicine. 50(6). 491–507. 19 indexed citations
13.
Kuhn, Klaus A., et al.. (2007). MEDINFO 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics - Building Sustainable Health Systems. IOS Press eBooks. 129. 34 indexed citations
14.
Poh, Kim-Leng, et al.. (2005). Integration of Probabilistic Graphic Models for Decision Support. National Conference on Artificial Intelligence. 40–47.
15.
Li, Guoliang & Tze-Yun Leong. (2005). A Framework to Learn Bayesian Network from Changing, Multiple-Source Biomedical Data. National University of Singapore. 66–72. 1 indexed citations
16.
Lu, Tsai-Ching, Marek J. Drużdżel, & Tze-Yun Leong. (2000). Causal Mechanism-based Model Constructions. Uncertainty in Artificial Intelligence. 353–362. 7 indexed citations
17.
Leong, Tze-Yun. (1998). Multiple perspective dynamic decision making. Artificial Intelligence. 105(1-2). 209–261. 20 indexed citations
18.
Leong, Tze-Yun, et al.. (1997). Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 848–848. 5 indexed citations
19.
Leong, Tze-Yun. (1996). Multiple Perspective Reasoning. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 562–573. 3 indexed citations
20.
Wang, C.M., et al.. (1992). Optimization Techniques and Applications. 1–1264. 50 indexed citations

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.

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