Denver Dash
Impact in
- Artificial Intelligence top 5%
- Bayesian Modeling and Causal Inference
- Anomaly Detection Techniques and Applications
- Signal Processing top 10%
Papers in
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- Bayesian Modeling and Causal Inference 13
- Anomaly Detection Techniques and Applications 8
- Logic, Reasoning, and Knowledge 4
- AI-based Problem Solving and Planning 3
- Neural Networks and Applications 2
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- Data-Driven Disease Surveillance 5
- Co-authors
- Marek J. Drużdżel (8 shared papers)Gregory F. Cooper (4 shared papers)Weng‐Keen Wong (4 shared papers)William R. Hogan (2 shared papers)John Levander (2 shared papers)Michael M. Wagner (2 shared papers)Eve M. Schooler (2 shared papers)John Mark Agosta (2 shared papers)
- Journals
- Journal of Machine Learning Research (1 paper)Machine Learning (1 paper)IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (1 paper)Artificial Intelligence (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United StatesJapanPoland
In The Last Decade
Denver Dash
21 papers receiving 448 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 321
- Signal Processing 68
- Management Science and Operations Research 48
- Computer Networks and Communications 88
- Statistics and Probability 24
Countries citing papers authored by Denver Dash
This map shows the geographic impact of Denver Dash'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 Denver Dash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Denver Dash more than expected).
Fields of papers citing papers by Denver Dash
This network shows the impact of papers produced by Denver Dash. 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 Denver Dash. The network helps show where Denver Dash may publish in the future.
Co-authors
The 23 scholars most cited alongside Denver Dash, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 70 | |
| 2 | A hybrid anytime algorithm for the construction of causal models from sparse data | 1999 | 69 |
| 3 | 2004 | 62 | |
| 4 | When gossip is good: distributed probabilistic inference for detection of slow network intrusions | 2006 | 44 |
| 5 | 2012 | 44 | |
| 6 | Robust independence testing for constraint-based learning of causal structure | 2002 | 38 |
| 7 | Exact model averaging with naive Bayesian classifiers | 2002 | 29 |
| 8 | 2006 | 24 | |
| 9 | Restructuring Dynamic Causal Systems in Equilibrium. | 2005 | 22 |
| 10 | 2001 | 22 | |
| 11 | 2010 | 15 | |
| 12 | 2012 | 13 | |
| 13 | 2015 | 10 | |
| 14 | 2008 | 8 | |
| 15 | Relational learning for collective classification of entities in images | 2010 | 7 |
| 16 | Sequences of mechanisms for causal reasoning in artificial intelligence | 2013 | 7 |
| 17 | COD: online temporal clustering for outbreak detection | 2007 | 4 |
| 18 | 2006 | 3 | |
| 19 | Learning causal models that make correct manipulation predictions with time series data | 2008 | 2 |
| 20 | 2012 | 2 |
About Denver Dash
Denver Dash is a scholar working on Artificial Intelligence, Epidemiology, Signal Processing, Computer Networks and Communications and Computational Theory and Mathematics, having authored 23 papers that have together received 497 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (13 papers), Anomaly Detection Techniques and Applications (8 papers), Data-Driven Disease Surveillance (5 papers), Logic, Reasoning, and Knowledge (4 papers), AI-based Problem Solving and Planning (3 papers), Network Security and Intrusion Detection (3 papers), Multi-Criteria Decision Making (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (321 citations), Signal Processing (68 citations), Management Science and Operations Research (48 citations), Computer Networks and Communications (88 citations) and Statistics and Probability (24 citations). Denver Dash has collaborated with scholars based in United States, Japan and Poland. Frequent co-authors include Marek J. Drużdżel, Gregory F. Cooper, Weng‐Keen Wong, William R. Hogan, John Levander, Michael M. Wagner, Eve M. Schooler, John Mark Agosta, Steven P. Levitan and Yan Fang. Their work appears in journals such as Journal of Machine Learning Research, Machine Learning, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Artificial Intelligence and Lecture notes in computer science.
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.