Francis Ferraro
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Tim FininBenjamin Van DurmeRachel RudingerPushpendre RastogiPushmeet KohliAishwarya AgrawalIshan MisraMargaret Mitchell
- Topics
- Topic Modeling (22 papers)Natural Language Processing Techniques (21 papers)Multimodal Machine Learning Applications (10 papers)
- Journals
- OpticaTransactions of the Association for Computational LinguisticsJournal of Web Semantics
- Partner nations
- United StatesUnited KingdomBelgium
In The Last Decade
Francis Ferraro
33 papers receiving 364 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 262
- Computer Vision and Pattern Recognition 145
- Information Systems 68
- Computer Networks and Communications 35
- Signal Processing 23
Countries citing papers authored by Francis Ferraro
This map shows the geographic impact of Francis Ferraro'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 Francis Ferraro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francis Ferraro more than expected).
Fields of papers citing papers by Francis Ferraro
This network shows the impact of papers produced by Francis Ferraro. 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 Francis Ferraro. The network helps show where Francis Ferraro may publish in the future.
Co-authorship network of co-authors of Francis Ferraro
This figure shows the co-authorship network connecting the top 25 collaborators of Francis Ferraro. A scholar is included among the top collaborators of Francis Ferraro 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 Francis Ferraro. Francis Ferraro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 0 | |
| 11 | 62 | |
| 12 | 1 | |
| 13 | 2 | |
| 14 | 2 | |
| 15 | 138 | |
| 16 | On Available Corpora for Empirical Methods in Vision & Language. | 1 |
| 17 | 6 | |
| 18 | 59 | |
| 19 | Nerit: Named Entity Recognition for Informal Text | 10 |
| 20 | Judging Grammaticality with Count-Induced Tree Substitution Grammars | 3 |
About Francis Ferraro
Francis Ferraro is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 40 papers that have together received 383 indexed citations. Recurring topics across this work include Topic Modeling (22 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (10 papers). The work is most often cited by research in Artificial Intelligence (262 citations), Computer Vision and Pattern Recognition (145 citations) and Information Systems (68 citations). Francis Ferraro has collaborated with scholars based in United States, United Kingdom and Belgium. Frequent co-authors include Tim Finin, Benjamin Van Durme, Rachel Rudinger, Pushpendre Rastogi, Pushmeet Kohli, Aishwarya Agrawal, Ishan Misra, Margaret Mitchell, Xiaodong He and Dhruv Batra. Their work appears in journals such as Optica, Transactions of the Association for Computational Linguistics and Journal of Web Semantics.
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.