Sean Gerrish

2.4k total citations · 1 hit paper
7 papers, 1.6k citations indexed

About

Sean Gerrish is a scholar working on Artificial Intelligence, General Social Sciences and Political Science and International Relations. According to data from OpenAlex, Sean Gerrish has authored 7 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in General Social Sciences and 2 papers in Political Science and International Relations. Recurrent topics in Sean Gerrish's work include Computational and Text Analysis Methods (3 papers), Electoral Systems and Political Participation (2 papers) and Bayesian Methods and Mixture Models (2 papers). Sean Gerrish is often cited by papers focused on Computational and Text Analysis Methods (3 papers), Electoral Systems and Political Participation (2 papers) and Bayesian Methods and Mixture Models (2 papers). Sean Gerrish collaborates with scholars based in United States. Sean Gerrish's co-authors include David M. Blei, Jordan Boyd‐Graber, Jonathan Chang, Chong Wang, Michael J. Freedman, Prem Gopalan, David Mimno, Jiang Yang, Kevin Nam and Wei Xiao and has published in prestigious journals such as First Monday, Neural Information Processing Systems and International Conference on Machine Learning.

In The Last Decade

Sean Gerrish

7 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Reading Tea Leaves: How Humans Interpret Topic Models

2009 1.2k citations Jonathan Chang, Sean Gerrish et al. Neural Information Processing Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sean Gerrish United States	6	869	369	255	234	202	7	1.6k
Jey Han Lau Australia	22	1.8k 2.1×	307 0.8×	229 0.9×	457 2.0×	202 1.0×	76	2.3k
Andreas Both Germany	7	721 0.8×	250 0.7×	305 1.2×	258 1.1×	104 0.5×	23	1.4k
Xia Feng China	6	539 0.6×	150 0.4×	297 1.2×	273 1.2×	129 0.6×	20	1.3k
Michael Röder Germany	6	703 0.8×	250 0.7×	309 1.2×	248 1.1×	105 0.5×	22	1.4k
Νικόλαος Αλέτρας United Kingdom	16	812 0.9×	87 0.2×	179 0.7×	166 0.7×	105 0.5×	70	1.4k
Bryan Routledge United States	19	944 1.1×	86 0.2×	487 1.9×	285 1.2×	390 1.9×	45	2.7k
Gerhard Heyer Germany	12	437 0.5×	254 0.7×	277 1.1×	105 0.4×	31 0.2×	58	996
Kenneth E. Shirley United States	5	303 0.3×	151 0.4×	288 1.1×	110 0.5×	43 0.2×	9	958
Ramnath Balasubramanyan United States	7	808 0.9×	60 0.2×	362 1.4×	267 1.1×	430 2.1×	13	1.4k

Countries citing papers authored by Sean Gerrish

Since Specialization

Citations

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

Fields of papers citing papers by Sean Gerrish

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sean Gerrish

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

All Works

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7 of 7 papers shown

1.

Blei, David M. & Sean Gerrish. (2013). Applications of latent variable models in modeling influence and decision making. 2 indexed citations

2.

Gerrish, Sean & David M. Blei. (2012). How They Vote: Issue-Adjusted Models of Legislative Behavior. Neural Information Processing Systems. 25. 2753–2761. 58 indexed citations

3.

Gopalan, Prem, Sean Gerrish, Michael J. Freedman, David M. Blei, & David Mimno. (2012). Scalable Inference of Overlapping Communities. Neural Information Processing Systems. 25. 2249–2257. 50 indexed citations

4.

Gerrish, Sean & David M. Blei. (2011). Predicting Legislative Roll Calls from Text. International Conference on Machine Learning. 489–496. 102 indexed citations

5.

Gerrish, Sean & David M. Blei. (2010). A Language-based Approach to Measuring Scholarly Impact. International Conference on Machine Learning. 375–382. 89 indexed citations

6.

Adamic, Lada A., Wei Xiao, Jiang Yang, et al.. (2010). Individual focus and knowledge contribution. First Monday. 15 indexed citations

7.

Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan Boyd‐Graber, & David M. Blei. (2009). Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems. 22. 288–296. 1239 indexed citations breakdown →

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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