Mohit Iyyer

169 total papers · 7.1k total citations
61 papers, 2.0k citations indexed

About

Mohit Iyyer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Mohit Iyyer has authored 61 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Mohit Iyyer's work include Topic Modeling (52 papers), Natural Language Processing Techniques (46 papers) and Multimodal Machine Learning Applications (11 papers). Mohit Iyyer is often cited by papers focused on Topic Modeling (52 papers), Natural Language Processing Techniques (46 papers) and Multimodal Machine Learning Applications (11 papers). Mohit Iyyer collaborates with scholars based in United States, India and Canada. Mohit Iyyer's co-authors include Jordan Boyd‐Graber, Hal Daumé, Varun Manjunatha, Luke Zettlemoyer, Philip Resnik, Eunsol Choi, Wen-tau Yih, Peter K. Enns, Wen-tau Yih and Mark Yatskar and has published in prestigious journals such as Language Resources and Evaluation, arXiv (Cornell University) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Mohit Iyyer

52 papers receiving 1.9k citations

Hit Papers

Deep Unordered Compositio... 2015 2026 2018 2022 2015 2018 100 200 300 400

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Mohit Iyyer 1.8k 420 239 121 78 61 2.0k
Noah Constant 2.0k 1.2× 447 1.1× 269 1.1× 121 1.0× 40 0.5× 22 2.4k
Yinfei Yang 1.8k 1.0× 751 1.8× 312 1.3× 192 1.6× 38 0.5× 37 2.4k
Ellie Pavlick 2.4k 1.4× 522 1.2× 211 0.9× 85 0.7× 48 0.6× 71 2.7k
Dani Yogatama 1.9k 1.1× 223 0.5× 315 1.3× 132 1.1× 38 0.5× 30 2.2k
Andrea Madotto 1.6k 0.9× 272 0.6× 208 0.9× 132 1.1× 25 0.3× 42 2.3k
Anders Søgaard 2.5k 1.4× 388 0.9× 211 0.9× 78 0.6× 42 0.5× 195 2.8k
Yulia Tsvetkov 1.4k 0.8× 153 0.4× 156 0.7× 140 1.2× 69 0.9× 96 1.7k
Barbara Plank 1.8k 1.0× 349 0.8× 234 1.0× 146 1.2× 23 0.3× 143 2.2k
Roi Reichart 2.5k 1.4× 353 0.8× 192 0.8× 80 0.7× 33 0.4× 108 2.9k
Chenghua Lin 1.5k 0.8× 139 0.3× 284 1.2× 132 1.1× 64 0.8× 97 1.8k

Countries citing papers authored by Mohit Iyyer

Since Specialization
Citations

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

Fields of papers citing papers by Mohit Iyyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohit Iyyer

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

All Works

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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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