Mor Geva

1.6k total citations
29 papers, 383 citations indexed

About

Mor Geva is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Mor Geva has authored 29 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems. Recurrent topics in Mor Geva's work include Topic Modeling (25 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (5 papers). Mor Geva is often cited by papers focused on Topic Modeling (25 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (5 papers). Mor Geva collaborates with scholars based in United States, Israel and United Kingdom. Mor Geva's co-authors include Jonathan Berant, Yoav Goldberg, Amir Globerson, Avi Caciularu, Tushar Khot, Daniel Khashabi, Dan Roth, Kevin I‐Kai Wang, Daniel Deutch and Ankit Gupta and has published in prestigious journals such as Transactions of the Association for Computational Linguistics and Infoscience (Ecole Polytechnique Fédérale de Lausanne).

In The Last Decade

Mor Geva

24 papers receiving 368 citations

Peers

Mor Geva
Comparison fields: 5 of 56
  • Artificial Intelligence 329
  • Computer Vision and Pattern Recognition 85
  • Information Systems 41
  • Management Science and Operations Research 12
  • Safety Research 10
Replace Timo Schick with:
Timo Schick Germany
Niklas Muennighoff United States
Max Bartolo United Kingdom
Wangchunshu Zhou China
Hanjie Chen United States
Qingxiu Dong China
Kelvin Guu United States
Ori Ram Israel
Yeganeh Kordi United States
Alham Fikri Aji United Kingdom
Timo Schick Germany View profile →
Citations per field, relative to Mor Geva
Mor Geva · 1×
Citations per year, relative to Mor Geva
Mor Geva · 1×

Countries citing papers authored by Mor Geva

Since Specialization
Citations

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

Fields of papers citing papers by Mor Geva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mor Geva

This figure shows the co-authorship network connecting the top 25 collaborators of Mor Geva. A scholar is included among the top collaborators of Mor Geva 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 Mor Geva. Mor Geva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 2
2 10
3 0
4 0
5 0
6 4
7 1
8 1
9 13
10 2
11 9
12 6
13 19
14 22
15 24
16 2
17 4
18 53
19 8
20 27

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026