Jason Lowe-Power

926 citations
27 papers · 619 · h-index 10

Impact in

Papers in

Jason Lowe-Power

22 papers receiving 591 citations

Peers

Jason Lowe-Power
Comparison fields: 5 of 35
  • Hardware and Architecture 481
  • Computer Networks and Communications 436
  • Computational Mathematics 7
  • Information Systems 149
  • Artificial Intelligence 107
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Guoyang Chen United States
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Omer Khan United States
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Citations per field
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Citations per year

Countries citing papers authored by Jason Lowe-Power

Since Specialization
Citations

This map shows the geographic impact of Jason Lowe-Power'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 Jason Lowe-Power with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Lowe-Power more than expected).

Fields of papers citing papers by Jason Lowe-Power

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason Lowe-Power. 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 Jason Lowe-Power. The network helps show where Jason Lowe-Power may publish in the future.

Co-authors

The 25 scholars most cited alongside Jason Lowe-Power, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jason Lowe-Power Line = papers co-authored together Jason Lowe-Power links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014166
2 2014111
3 201399
4 202046
5 201538
6 202124
7 201922
8 202120
9 201819
10 201514
11 20159
12 20188
13 20198
14 20217
15 20236
16 20226
17 20235
18 20183
19 20182
20 20222

About Jason Lowe-Power

Jason Lowe-Power is a scholar working on Computer Networks and Communications, Hardware and Architecture, Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 27 papers that have together received 619 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (20 papers), Advanced Data Storage Technologies (15 papers), Cloud Computing and Resource Management (5 papers), Interconnection Networks and Systems (5 papers), Graph Theory and Algorithms (4 papers), Security and Verification in Computing (4 papers), Embedded Systems Design Techniques (3 papers) and Distributed systems and fault tolerance (3 papers). The work is most often cited by research in Hardware and Architecture (481 citations), Computer Networks and Communications (436 citations), Computational Mathematics (7 citations), Information Systems (149 citations) and Artificial Intelligence (107 citations). Jason Lowe-Power has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include David A. Wood, Joel Hestness, Marc S. Orr, Venkatesh Akella, Junli Gu, Steven K. Reinhardt, Arkaprava Basu, Bradford M. Beckmann, Jawad Khan and Ayaz Akram. Their work appears in journals such as IEEE Computer Architecture Letters, Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, ACM SIGMOD Record, ACM SIGPLAN Notices and eScholarship (California Digital Library).

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.

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