David J. Marchette
- Artificial Intelligence top 2%
- Computer Networks and Communications top 5%
- Statistical and Nonlinear Physics top 2%
- Computer Vision and Pattern Recognition top 5%
- Statistics and Probability top 2%
- Co-authors
- Carey E. PriebeJohn C. WiermanYoungser ParkJohn M. ConroyEdward J. WegmanLancelot F. JamesJeffrey L. SolkaBradley C. Wallet
- Topics
- Bayesian Methods and Mixture Models (11 papers)Neural Networks and Applications (11 papers)Anomaly Detection Techniques and Applications (10 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceTechnometricsPattern Recognition
- Partner nations
- United StatesRussiaTürkiye
In The Last Decade
David J. Marchette
72 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 132
- Artificial Intelligence 612
- Computer Networks and Communications 307
- Statistical and Nonlinear Physics 288
- Computer Vision and Pattern Recognition 186
- Statistics and Probability 162
Countries citing papers authored by David J. Marchette
This map shows the geographic impact of David J. Marchette'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 David J. Marchette with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David J. Marchette more than expected).
Fields of papers citing papers by David J. Marchette
This network shows the impact of papers produced by David J. Marchette. 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 David J. Marchette. The network helps show where David J. Marchette may publish in the future.
Co-authorship network of co-authors of David J. Marchette
This figure shows the co-authorship network connecting the top 25 collaborators of David J. Marchette. A scholar is included among the top collaborators of David J. Marchette 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 David J. Marchette. David J. Marchette is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 11 | |
| 5 | Utilizing covariates in partially observed networks | 1 |
| 6 | A limit theorem for scaled eigenvectors of random dot product graphs | 3 |
| 7 | 16 | |
| 8 | 7 | |
| 9 | Fusion of disparate information through joint embeddings | 1 |
| 10 | 1 | |
| 11 | 8 | |
| 12 | 22 | |
| 13 | 30 | |
| 14 | 40 | |
| 15 | 23 | |
| 16 | 5 | |
| 17 | 2 | |
| 18 | 2 | |
| 19 | A Genetic Algorithm for Best Subset Selection in Linear Regression | 22 |
| 20 | 7 |
About David J. Marchette
David J. Marchette is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 72 papers that have together received 1.3k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (11 papers), Neural Networks and Applications (11 papers) and Anomaly Detection Techniques and Applications (10 papers). The work is most often cited by research in Statistical and Nonlinear Physics (288 citations), Computational Mathematics (12 citations) and Statistics and Probability (162 citations). David J. Marchette has collaborated with scholars based in United States, Russia and Türkiye. Frequent co-authors include Carey E. Priebe, John C. Wierman, Youngser Park, John M. Conroy, Edward J. Wegman, Lancelot F. James, Jeffrey L. Solka, Bradley C. Wallet, Diego A. Socolinsky and Vijayan N. Nair. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Technometrics and Pattern Recognition.
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.