Dave Higdon
Impact in
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- Probabilistic and Robust Engineering Design
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
Papers in
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- Gaussian Processes and Bayesian Inference 6
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- Meteorological Phenomena and Simulations 7
- Co-authors
- Brian J. Williams (4 shared papers)James Gattiker (5 shared papers)Maria Rightley (1 shared paper)Marc C. Kennedy (1 shared paper)James C. Cavendish (1 shared paper)John A. Cafeo (1 shared paper)Robert D. Ryne (1 shared paper)Madhav Marathe (4 shared papers)
- Journals
- Journal of the American Statistical Association (3 papers)Bayesian Analysis (3 papers)Technometrics (3 papers)The Astrophysical Journal (2 papers)Journal of Computational and Graphical Statistics (2 papers)
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Dave Higdon
32 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Statistics, Probability and Uncertainty 646
- Modeling and Simulation 214
- Computational Theory and Mathematics 460
- Management Science and Operations Research 266
- Statistical and Nonlinear Physics 173
Countries citing papers authored by Dave Higdon
This map shows the geographic impact of Dave Higdon'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 Dave Higdon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dave Higdon more than expected).
Fields of papers citing papers by Dave Higdon
This network shows the impact of papers produced by Dave Higdon. 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 Dave Higdon. The network helps show where Dave Higdon may publish in the future.
Co-authors
The 25 scholars most cited alongside Dave Higdon, 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 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Computer Model Calibration Using High-Dimensional Output Hit paper breakdown → | 2008 | 574 |
| 2 | 2004 | 438 | |
| 3 | 2017 | 118 | |
| 4 | 2013 | 102 | |
| 5 | 2008 | 95 | |
| 6 | 2017 | 93 | |
| 7 | 2006 | 63 | |
| 8 | 2002 | 60 | |
| 9 | 2016 | 51 | |
| 10 | 2021 | 47 | |
| 11 | 2022 | 45 | |
| 12 | 2019 | 33 | |
| 13 | 2005 | 26 | |
| 14 | 2013 | 20 | |
| 15 | 2015 | 20 | |
| 16 | 2015 | 19 | |
| 17 | 2015 | 16 | |
| 18 | 2021 | 15 | |
| 19 | 2006 | 13 | |
| 20 | 2017 | 10 |
About Dave Higdon
Dave Higdon is a scholar working on Artificial Intelligence, Atmospheric Science, Statistics, Probability and Uncertainty, Global and Planetary Change and Epidemiology, having authored 36 papers that have together received 1.9k indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (9 papers), Meteorological Phenomena and Simulations (7 papers), Gaussian Processes and Bayesian Inference (6 papers), Climate variability and models (5 papers), Data-Driven Disease Surveillance (5 papers), Reservoir Engineering and Simulation Methods (4 papers), COVID-19 epidemiological studies (4 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (646 citations), Modeling and Simulation (214 citations), Computational Theory and Mathematics (460 citations), Management Science and Operations Research (266 citations) and Statistical and Nonlinear Physics (173 citations). Dave Higdon has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Brian J. Williams, James Gattiker, Maria Rightley, Marc C. Kennedy, James C. Cavendish, John A. Cafeo, Robert D. Ryne, Madhav Marathe, Jiangzhuo Chen and Bryan Lewis. Their work appears in journals such as Journal of the American Statistical Association, Bayesian Analysis, Technometrics, The Astrophysical Journal and Journal of Computational and Graphical Statistics.
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.