Ryan Hafen
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Epidemiology
- Public Health, Environmental and Occupational Health
- Co-authors
- William S. ClevelandValerie DaggettEric MerkleyAbdullah KahramanSteven J. RysavyJoshua AdkinsRoss MaciejewskiDavid S. Ebert
- Topics
- Energy Load and Power Forecasting (6 papers)Data Visualization and Analytics (6 papers)Electric Power System Optimization (6 papers)
- Partner nations
- United StatesUnited KingdomPoland
In The Last Decade
Ryan Hafen
27 papers receiving 984 citations
Peers
Comparison fields: 5 of 159
- Molecular Biology 283
- Computer Vision and Pattern Recognition 162
- Artificial Intelligence 151
- Epidemiology 118
- Public Health, Environmental and Occupational Health 102
Countries citing papers authored by Ryan Hafen
This map shows the geographic impact of Ryan Hafen'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 Ryan Hafen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Hafen more than expected).
Fields of papers citing papers by Ryan Hafen
This network shows the impact of papers produced by Ryan Hafen. 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 Ryan Hafen. The network helps show where Ryan Hafen may publish in the future.
Co-authorship network of co-authors of Ryan Hafen
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Hafen. A scholar is included among the top collaborators of Ryan Hafen 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 Ryan Hafen. Ryan Hafen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 3 | |
| 4 | 14 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 24 | |
| 8 | 136 | |
| 9 | 64 | |
| 10 | 28 | |
| 11 | 12 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 58 | |
| 15 | Local regression models: Advancements, applications, and new methods | 10 |
| 16 | Visualization Databases for the Analysis of Large Complex Datasets | 14 |
| 17 | 46 | |
| 18 | 16 | |
| 19 | 110 | |
| 20 | 19 |
About Ryan Hafen
Ryan Hafen is a scholar working on Ecological Modeling, Safety, Risk, Reliability and Quality and Artificial Intelligence, having authored 28 papers that have together received 1.0k indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (6 papers), Data Visualization and Analytics (6 papers) and Electric Power System Optimization (6 papers). The work is most often cited by research in Parasitology (55 citations), Computer Vision and Pattern Recognition (162 citations) and Signal Processing (76 citations). Ryan Hafen has collaborated with scholars based in United States, United Kingdom and Poland. Frequent co-authors include William S. Cleveland, Valerie Daggett, Eric Merkley, Abdullah Kahraman, Steven J. Rysavy, Joshua Adkins, Ross Maciejewski, David S. Ebert, Daniel E. Lee and Bryan A. Liang. Their work appears in journals such as Clinical Microbiology Reviews, Statistics in Medicine and Protein 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.