Joel Mackenzie

626 citations
32 papers · 229 · h-index 10

Impact in

Papers in

Joel Mackenzie

31 papers receiving 223 citations

Peers

Joel Mackenzie
Comparison fields: 5 of 31
  • Signal Processing 65
  • Information Systems 108
  • Artificial Intelligence 137
  • Computer Vision and Pattern Recognition 83
  • Computer Networks and Communications 46
Replace Fei Cai with:
Fei Cai China
Thomas Roelleke United Kingdom
Eugene Bagdasaryan United States
Tomáš Kočiský United Kingdom
Srinivasan H. Sengamedu United States
Matt Crane New Zealand
Hoa T. Nguyen United States
Thanh Tran Germany
Catherine Berrut France
Weiyi Meng United States
Joel Mackenzie relative to Fei Cai China Fei Cai's profile →
Citations per field
00.5×4.3×
Fei Cai · 1×
Citations per year

Countries citing papers authored by Joel Mackenzie

Since Specialization
Citations

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

Fields of papers citing papers by Joel Mackenzie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joel Mackenzie. 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 Joel Mackenzie. The network helps show where Joel Mackenzie may publish in the future.

Co-authors

The 25 scholars most cited alongside Joel Mackenzie, 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 Joel Mackenzie Line = papers co-authored together Joel Mackenzie links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202031
2 201729
3 201918
4 202217
5 202015
6 202215
7 201915
8 202110
9 20249
10 20209
11 20228
12 20157
13 20226
14 20225
15
Towards Efficient and Effective Query Variant Generation
20185
16 20234
17 20174
18 20214
19 20193
20 20223

About Joel Mackenzie

Joel Mackenzie is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 32 papers that have together received 229 indexed citations. Recurring topics across this work include Data Management and Algorithms (12 papers), Information Retrieval and Search Behavior (12 papers), Advanced Image and Video Retrieval Techniques (9 papers), Advanced Database Systems and Queries (6 papers), Topic Modeling (6 papers), Algorithms and Data Compression (6 papers), Data Quality and Management (4 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Signal Processing (65 citations), Information Systems (108 citations), Artificial Intelligence (137 citations), Computer Vision and Pattern Recognition (83 citations) and Computer Networks and Communications (46 citations). Joel Mackenzie has collaborated with scholars based in Australia, United States and Italy. Frequent co-authors include Alistair Moffat, J. Shane Culpepper, Matthias Petri, Antonio Mallia, Torsten Suel, Johanne R. Trippas, Jimmy Lin, Andrew Trotman, Leif Azzopardi and Matt Crane. Their work appears in journals such as Information Retrieval, ACM Transactions on Information Systems, Information Processing & Management, IEEE Transactions on Knowledge and Data Engineering and ISTI Open Portal.

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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