Douglas Allaire
- Computational Theory and Mathematics top 1%
- Statistics, Probability and Uncertainty top 0.5%
- Materials Chemistry
- Mechanical Engineering top 10%
- Artificial Intelligence top 10%
- Co-authors
- Karen WillcoxRaymundo ArróyaveDanial KhatamsazAnkit SrivastavaPrashant SinghBrent VelaD. D. JohnsonSam Friedman
- Topics
- Advanced Multi-Objective Optimization Algorithms (39 papers)Probabilistic and Robust Engineering Design (39 papers)Machine Learning in Materials Science (15 papers)
- Cited by
- Statistics, Probability and UncertaintyComputational Theory and MathematicsManagement Science and Operations Research
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Douglas Allaire
84 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 104
- Computational Theory and Mathematics 429
- Statistics, Probability and Uncertainty 415
- Materials Chemistry 299
- Mechanical Engineering 259
- Artificial Intelligence 201
Countries citing papers authored by Douglas Allaire
This map shows the geographic impact of Douglas Allaire'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 Douglas Allaire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Douglas Allaire more than expected).
Fields of papers citing papers by Douglas Allaire
This network shows the impact of papers produced by Douglas Allaire. 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 Douglas Allaire. The network helps show where Douglas Allaire may publish in the future.
Co-authorship network of co-authors of Douglas Allaire
This figure shows the co-authorship network connecting the top 25 collaborators of Douglas Allaire. A scholar is included among the top collaborators of Douglas Allaire 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 Douglas Allaire. Douglas Allaire is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 27 | |
| 8 | 64 | |
| 9 | 3 | |
| 10 | 12 | |
| 11 | 24 | |
| 12 | 16 | |
| 13 | 1 | |
| 14 | 9 | |
| 15 | 5 | |
| 16 | 25 | |
| 17 | 23 | |
| 18 | 23 | |
| 19 | 20 | |
| 20 | Fusing information from multifidelity computer models of physical systems | 11 |
About Douglas Allaire
Douglas Allaire is a scholar working on Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Metals and Alloys, having authored 91 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (39 papers), Probabilistic and Robust Engineering Design (39 papers) and Machine Learning in Materials Science (15 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (415 citations), Computational Theory and Mathematics (429 citations) and Management Science and Operations Research (197 citations). Douglas Allaire has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Karen Willcox, Raymundo Arróyave, Danial Khatamsaz, Ankit Srivastava, Prashant Singh, Brent Vela, D. D. Johnson, Sam Friedman, Seyede Fatemeh Ghoreishi and Rémi Lam. Their work appears in journals such as PLoS ONE, Journal of Applied Physics and Acta Materialia.
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.