Kevin Judd
Impact in
- Statistical and Nonlinear Physics top 0.5%
- Chaos control and synchronization
- Signal Processing top 2%
- Time Series Analysis and Forecasting
Papers in
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- Chaos control and synchronization 28
-
- Time Series Analysis and Forecasting 16
- Co-authors
- Alistair MeesMichael SmallLeonard A. SmithLiangyue CaoTimothy W. McLainThomas StemlerKazuyuki AiharaCarolyn A. Reynolds
- Journals
- Physica D Nonlinear Phenomena (20 papers)International Journal of Bifurcation and Chaos (14 papers)Physics Letters A (5 papers)Neural Networks (3 papers)Journal of the Atmospheric Sciences (3 papers)
- Partner nations
- AustraliaJapanUnited States
In The Last Decade
Kevin Judd
83 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 137
- Statistical and Nonlinear Physics 863
- Signal Processing 273
- Economics and Econometrics 567
- Artificial Intelligence 505
- Atmospheric Science 249
Countries citing papers authored by Kevin Judd
This map shows the geographic impact of Kevin Judd'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 Kevin Judd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Judd more than expected).
Fields of papers citing papers by Kevin Judd
This network shows the impact of papers produced by Kevin Judd. 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 Kevin Judd. The network helps show where Kevin Judd may publish in the future.
Co-authors
The 25 scholars most cited alongside Kevin Judd, 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 | 2015 | 17 | |
| 2 | Coupling Climate Models and Forward-Looking Economic Models | 2010 | 1 |
| 3 | 2009 | 19 | |
| 4 | 2009 | 8 | |
| 5 | 2008 | 21 | |
| 6 | 2007 | 14 | |
| 7 | 2007 | 14 | |
| 8 | 2007 | 77 | |
| 9 | 2005 | 14 | |
| 10 | 2004 | 64 | |
| 11 | 2004 | 7 | |
| 12 | 2004 | 23 | |
| 13 | 2003 | 30 | |
| 14 | 2002 | 23 | |
| 15 | CalMaeth: an interactive learning system focussing on the diagnosis of mathematical misconceptions | 2001 | 2 |
| 16 | 1998 | 42 | |
| 17 | 1997 | 9 | |
| 18 | 1997 | 8 | |
| 19 | 1995 | 6 | |
| 20 | 1995 | 20 |
About Kevin Judd
Kevin Judd is a scholar working on Statistical and Nonlinear Physics, Signal Processing, Artificial Intelligence, Atmospheric Science and Economics and Econometrics, having authored 86 papers that have together received 2.1k indexed citations. Recurring topics across this work include Chaos control and synchronization (28 papers), Complex Systems and Time Series Analysis (20 papers), Neural Networks and Applications (19 papers), Time Series Analysis and Forecasting (16 papers), Meteorological Phenomena and Simulations (14 papers), Climate variability and models (12 papers), Nonlinear Dynamics and Pattern Formation (10 papers) and Neural dynamics and brain function (10 papers). The work is most often cited by research in Statistical and Nonlinear Physics (863 citations), Signal Processing (273 citations), Economics and Econometrics (567 citations), Artificial Intelligence (505 citations) and Atmospheric Science (249 citations). Kevin Judd has collaborated with scholars based in Australia, Japan and United States. Frequent co-authors include Alistair Mees, Michael Small, Leonard A. Smith, Liangyue Cao, Timothy W. McLain, Thomas Stemler, Kazuyuki Aihara, Carolyn A. Reynolds, Yoshito Hirata and J. Teixeira. Their work appears in journals such as Physica D Nonlinear Phenomena, International Journal of Bifurcation and Chaos, Physics Letters A, Neural Networks and Journal of the Atmospheric Sciences.
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.