Jeff M. Phillips
Impact in
- Signal Processing top 2%
- Data Management and Algorithms
- Computational Mathematics top 10%
Papers in
-
- Algorithms and Data Compression 6
- Topic Modeling 5
-
- Data Visualization and Analytics 5
- Co-authors
- Suresh Venkatasubramanian (8 shared papers)Feifei Li (6 shared papers)Carlo Tomasi (1 shared paper)Ran Liu (1 shared paper)Dong Xie (4 shared papers)Sunipa Dev (5 shared papers)Pankaj K. Agarwal (6 shared papers)Lydia E. Kavraki (2 shared papers)
- Journals
- Proceedings of the VLDB Endowment (3 papers)Computers Environment and Urban Systems (2 papers)IEEE Transactions on Knowledge and Data Engineering (2 papers)SIAM Journal on Computing (2 papers)The VLDB Journal (1 paper)
- Partner nations
- United StatesChinaDenmark
In The Last Decade
Jeff M. Phillips
66 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 112
- Signal Processing 351
- Computational Mathematics 16
- Computer Vision and Pattern Recognition 414
- Artificial Intelligence 565
- Computer Graphics and Computer-Aided Design 61
Countries citing papers authored by Jeff M. Phillips
This map shows the geographic impact of Jeff M. Phillips'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 Jeff M. Phillips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff M. Phillips more than expected).
Fields of papers citing papers by Jeff M. Phillips
This network shows the impact of papers produced by Jeff M. Phillips. 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 Jeff M. Phillips. The network helps show where Jeff M. Phillips may publish in the future.
Co-authors
The 25 scholars most cited alongside Jeff M. Phillips, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 125 | |
| 2 | 2013 | 101 | |
| 3 | 2017 | 100 | |
| 4 | 2021 | 81 | |
| 5 | 2013 | 81 | |
| 6 | 2016 | 71 | |
| 7 | 2020 | 62 | |
| 8 | 2012 | 61 | |
| 9 | 2004 | 58 | |
| 10 | 2013 | 47 | |
| 11 | 2020 | 45 | |
| 12 | 2006 | 44 | |
| 13 | 2018 | 29 | |
| 14 | 2008 | 28 | |
| 15 | 2006 | 25 | |
| 16 | 2012 | 25 | |
| 17 | 2003 | 24 | |
| 18 | 2011 | 23 | |
| 19 | 2021 | 21 | |
| 20 | 2012 | 18 |
About Jeff M. Phillips
Jeff M. Phillips is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications and Computational Theory and Mathematics, having authored 72 papers that have together received 1.4k indexed citations. Recurring topics across this work include Data Management and Algorithms (17 papers), Advanced Database Systems and Queries (7 papers), Algorithms and Data Compression (6 papers), Data Visualization and Analytics (5 papers), Sparse and Compressive Sensing Techniques (5 papers), Computational Geometry and Mesh Generation (5 papers), Data-Driven Disease Surveillance (5 papers) and Topic Modeling (5 papers). The work is most often cited by research in Signal Processing (351 citations), Computational Mathematics (16 citations), Computer Vision and Pattern Recognition (414 citations), Artificial Intelligence (565 citations) and Computer Graphics and Computer-Aided Design (61 citations). Jeff M. Phillips has collaborated with scholars based in United States, China and Denmark. Frequent co-authors include Suresh Venkatasubramanian, Feifei Li, Carlo Tomasi, Ran Liu, Dong Xie, Sunipa Dev, Pankaj K. Agarwal, Lydia E. Kavraki, Nazareth Bedrossian and Ke Yi. Their work appears in journals such as Proceedings of the VLDB Endowment, Computers Environment and Urban Systems, IEEE Transactions on Knowledge and Data Engineering, SIAM Journal on Computing and The VLDB Journal.
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.