Jeremy Archuleta
- Computer Networks and Communications top 5%
- Information Systems top 5%
- Molecular Biology
- Hardware and Architecture top 10%
- Artificial Intelligence
- Co-authors
- Wu-chun FengHeshan LinMark K. GardnerJoão Carlos SetúbalZhe ZhangXiaosong MaAndrew WarrenChung‐Hsing Hsu
- Topics
- Genomics and Phylogenetic Studies (6 papers)Algorithms and Data Compression (5 papers)Distributed and Parallel Computing Systems (4 papers)
- Journals
- BMC BioinformaticsInternational Journal of Parallel ProgrammingConference proceedings
- Partner nations
- United States
In The Last Decade
Jeremy Archuleta
13 papers receiving 297 citations
Peers
Comparison fields: 5 of 50
- Computer Networks and Communications 187
- Information Systems 153
- Molecular Biology 90
- Hardware and Architecture 76
- Artificial Intelligence 41
Countries citing papers authored by Jeremy Archuleta
This map shows the geographic impact of Jeremy Archuleta'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 Jeremy Archuleta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeremy Archuleta more than expected).
Fields of papers citing papers by Jeremy Archuleta
This network shows the impact of papers produced by Jeremy Archuleta. 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 Jeremy Archuleta. The network helps show where Jeremy Archuleta may publish in the future.
Co-authorship network of co-authors of Jeremy Archuleta
This figure shows the co-authorship network connecting the top 25 collaborators of Jeremy Archuleta. A scholar is included among the top collaborators of Jeremy Archuleta 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 Jeremy Archuleta. Jeremy Archuleta 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 | 73 | |
| 3 | Missing genes in the annotation of prokaryotic | 1 |
| 4 | 4 | |
| 5 | 112 | |
| 6 | 12 | |
| 7 | Distributed I/O with ParaMEDIC: Experiences with a Worldwide Supercomputer | 1 |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 5 | |
| 11 | 7 | |
| 12 | 33 | |
| 13 | 57 |
About Jeremy Archuleta
Jeremy Archuleta is a scholar working on Computer Networks and Communications, Hardware and Architecture and Artificial Intelligence, having authored 13 papers that have together received 315 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (6 papers), Algorithms and Data Compression (5 papers) and Distributed and Parallel Computing Systems (4 papers). The work is most often cited by research in Hardware and Architecture (76 citations), Computer Networks and Communications (187 citations) and Information Systems (153 citations). Jeremy Archuleta has collaborated with scholars based in United States. Frequent co-authors include Wu-chun Feng, Heshan Lin, Mark K. Gardner, João Carlos Setúbal, Zhe Zhang, Xiaosong Ma, Andrew Warren, Chung‐Hsing Hsu, Yong Cao and Tom Scogland. Their work appears in journals such as BMC Bioinformatics, International Journal of Parallel Programming and Conference proceedings.
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.