Tom Hope
Impact in
- Health Informatics top 10%
- Computer Science Applications top 10%
- Open Source Software Innovations
Papers in
-
- Topic Modeling 10
- Advanced Text Analysis Techniques 8
- Natural Language Processing Techniques 4
-
- Software Engineering Research 3
- Co-authors
- Dafna Shahaf (8 shared papers)Joel Chan (6 shared papers)Aniket Kittur (6 shared papers)Takuichi Nishimura (6 shared papers)Daniel S. Weld (6 shared papers)Masahiro Hamasaki (5 shared papers)Eric Horvitz (4 shared papers)Joseph Chee Chang (2 shared papers)
- Journals
- Artificial Intelligence in Medicine (1 paper)Cell Reports Medicine (1 paper)Research Involvement and Engagement (1 paper)Journal of Contemporary Religion (1 paper)Patterns (1 paper)
- Partner nations
- United StatesIsraelJapan
In The Last Decade
Tom Hope
38 papers receiving 394 citations
Peers
Comparison fields: 5 of 94
- Health Informatics 12
- Computer Science Applications 36
- Human-Computer Interaction 32
- Artificial Intelligence 178
- Information Systems and Management 25
Countries citing papers authored by Tom Hope
This map shows the geographic impact of Tom Hope'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 Tom Hope with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Hope more than expected).
Fields of papers citing papers by Tom Hope
This network shows the impact of papers produced by Tom Hope. 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 Tom Hope. The network helps show where Tom Hope may publish in the future.
Co-authors
The 25 scholars most cited alongside Tom Hope, 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 43 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 55 | |
| 2 | 2018 | 40 | |
| 3 | 2017 | 36 | |
| 4 | 2009 | 25 | |
| 5 | 2022 | 22 | |
| 6 | 2006 | 16 | |
| 7 | 2022 | 15 | |
| 8 | 2021 | 15 | |
| 9 | 2022 | 15 | |
| 10 | 2023 | 14 | |
| 11 | 2022 | 14 | |
| 12 | 2022 | 13 | |
| 13 | 2024 | 13 | |
| 14 | 2009 | 12 | |
| 15 | 2020 | 11 | |
| 16 | 2015 | 10 | |
| 17 | 2018 | 10 | |
| 18 | 2007 | 8 | |
| 19 | 2022 | 8 | |
| 20 | 2022 | 7 |
About Tom Hope
Tom Hope is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 43 papers that have together received 407 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Advanced Text Analysis Techniques (8 papers), Biomedical Text Mining and Ontologies (5 papers), Natural Language Processing Techniques (4 papers), Software Engineering Research (3 papers), Innovative Human-Technology Interaction (3 papers), Data Visualization and Analytics (3 papers) and Computational Drug Discovery Methods (3 papers). The work is most often cited by research in Health Informatics (12 citations), Computer Science Applications (36 citations), Human-Computer Interaction (32 citations), Artificial Intelligence (178 citations) and Information Systems and Management (25 citations). Tom Hope has collaborated with scholars based in United States, Israel and Japan. Frequent co-authors include Dafna Shahaf, Joel Chan, Aniket Kittur, Takuichi Nishimura, Daniel S. Weld, Masahiro Hamasaki, Eric Horvitz, Joseph Chee Chang, Sravanthi Parasa and Lixiu Yu. Their work appears in journals such as Artificial Intelligence in Medicine, Cell Reports Medicine, Research Involvement and Engagement, Journal of Contemporary Religion and Patterns.
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.