Nicholas K. Jong
Impact in
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- IoT and Edge/Fog Computing
- Software System Performance and Reliability
- Distributed and Parallel Computing Systems
- Information Systems top 5%
- Cloud Computing and Resource Management
Papers in
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- Reinforcement Learning in Robotics 7
- Evolutionary Algorithms and Applications 4
- Data Stream Mining Techniques 2
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- Advanced Bandit Algorithms Research 2
- Co-authors
- Peter StoneMohamed N. BennaniG. TesauroR. DasPatrick BeesonBenjamin KuipersRajarshi DasGerald Tesauro
- Journals
- Robotics and Autonomous Systems (1 paper)Cluster Computing (1 paper)International Joint Conference on Artificial Intelligence (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesNetherlands
In The Last Decade
Nicholas K. Jong
12 papers receiving 614 citations
Peers
Comparison fields: 5 of 54
- Computer Networks and Communications 297
- Information Systems 244
- Artificial Intelligence 305
- Computer Vision and Pattern Recognition 128
- Aerospace Engineering 101
Countries citing papers authored by Nicholas K. Jong
This map shows the geographic impact of Nicholas K. Jong'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 Nicholas K. Jong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas K. Jong more than expected).
Fields of papers citing papers by Nicholas K. Jong
This network shows the impact of papers produced by Nicholas K. Jong. 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 Nicholas K. Jong. The network helps show where Nicholas K. Jong may publish in the future.
Co-authors
The 15 scholars most cited alongside Nicholas K. Jong, 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 | 2008 | 21 | |
| 2 | 2008 | 24 | |
| 3 | 2007 | 80 | |
| 4 | 2007 | 36 | |
| 5 | Kernel-Based Models for Reinforcement Learning | 2006 | 14 |
| 6 | 2006 | 11 | |
| 7 | 2006 | 230 | |
| 8 | 2006 | 126 | |
| 9 | State abstraction discovery from irrelevant state variables | 2005 | 57 |
| 10 | Bayesian Models of Nonstationary Markov Decision Processes | 2005 | 3 |
| 11 | Online Performance Management Using Hybrid Reinforcement Learning | 2005 | 2 |
| 12 | Learning predictive state representations | 2003 | 69 |
About Nicholas K. Jong
Nicholas K. Jong is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computational Theory and Mathematics and Management Information Systems, having authored 12 papers that have together received 673 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Evolutionary Algorithms and Applications (4 papers), Software System Performance and Reliability (3 papers), Cloud Computing and Resource Management (2 papers), Data Stream Mining Techniques (2 papers), Robotics and Sensor-Based Localization (2 papers), Advanced Bandit Algorithms Research (2 papers) and Indoor and Outdoor Localization Technologies (1 paper). The work is most often cited by research in Computer Networks and Communications (297 citations), Information Systems (244 citations), Artificial Intelligence (305 citations), Computer Vision and Pattern Recognition (128 citations) and Aerospace Engineering (101 citations). Nicholas K. Jong has collaborated with scholars based in United States and Netherlands. Frequent co-authors include Peter Stone, Mohamed N. Bennani, G. Tesauro, R. Das, Patrick Beeson, Benjamin Kuipers, Rajarshi Das, Gerald Tesauro, David Pardoe and Satinder Singh. Their work appears in journals such as Robotics and Autonomous Systems, Cluster Computing, International Joint Conference on Artificial Intelligence and International Conference on Machine Learning.
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.