Emily Reif

4.2k total citations · 1 hit paper
17 papers, 802 citations indexed

About

Emily Reif is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Emily Reif has authored 17 papers receiving a total of 802 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Information Systems and 2 papers in Molecular Biology. Recurrent topics in Emily Reif's work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Software Engineering Research (3 papers). Emily Reif is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Software Engineering Research (3 papers). Emily Reif collaborates with scholars based in United States and United Kingdom. Emily Reif's co-authors include Andy Coenen, Ann Yuan, Adam Pearce, Daphne Ippolito, Been Kim, Martin Wattenberg, Benjamín Sánchez-Lengeling, Fernanda Viégas, Michael Terry and Greg S. Corrado and has published in prestigious journals such as IEEE Transactions on Visualization and Computer Graphics, Computational Brain & Behavior and Neural Information Processing Systems.

In The Last Decade

Emily Reif

16 papers receiving 772 citations

Hit Papers

align trajectories sort by overperformance

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.

Wordcraft: Story Writing With Large Language Models

2022 174 citations Ann Yuan, Andy Coenen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emily Reif United States	10	456	110	91	74	72	17	802
J.D. Zamfirescu-Pereira United States	9	266 0.6×	58 0.5×	53 0.6×	90 1.2×	49 0.7×	22	578
Ute Schmid Germany	17	488 1.1×	95 0.9×	50 0.5×	106 1.4×	42 0.6×	100	870
Joon Sung Park United States	4	326 0.7×	65 0.6×	30 0.3×	61 0.8×	59 0.8×	6	642
Eduardo Mosqueira-Rey Spain	11	238 0.5×	58 0.5×	61 0.7×	116 1.6×	36 0.5×	31	669
Kartik Talamadupula United States	16	636 1.4×	147 1.3×	27 0.3×	73 1.0×	49 0.7×	55	841
Shivani Kapania United States	8	233 0.5×	95 0.9×	61 0.7×	77 1.0×	172 2.4×	14	610
Ján Schneider Germany	14	303 0.7×	74 0.7×	62 0.7×	125 1.7×	22 0.3×	49	846
Teo Sušnjak New Zealand	19	383 0.8×	138 1.3×	108 1.2×	209 2.8×	48 0.7×	69	1.2k
David Alonso-Ríos Spain	9	201 0.4×	52 0.5×	60 0.7×	93 1.3×	36 0.5×	22	581
Gagan Bansal United States	11	471 1.0×	58 0.5×	111 1.2×	62 0.8×	239 3.3×	23	770

Countries citing papers authored by Emily Reif

Since Specialization

Citations

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

Fields of papers citing papers by Emily Reif

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emily Reif

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

All Works

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17 of 17 papers shown

Liu, Weixuan, et al.. (2026). RDoFlow: Automatically assessing under-specified statistical analyses in HCI. 683–698.

Kahng, Minsuk, Ian Tenney, Mahima Pushkarna, et al.. (2024). LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models. 1–7. 13 indexed citations

Reif, Emily, et al.. (2024). Automatic Histograms: Leveraging Language Models for Text Dataset Exploration. 1–9. 3 indexed citations

Kahng, Minsuk, Ian Tenney, Mahima Pushkarna, et al.. (2024). LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models. IEEE Transactions on Visualization and Computer Graphics. 31(1). 503–513. 7 indexed citations

Longpre, Shayne, Emily Reif, Katherine Lee, et al.. (2024). A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity. 3245–3276. 11 indexed citations

Reif, Emily, et al.. (2023). Data Similarity is Not Enough to Explain Language Model Performance. 11295–11304. 1 indexed citations

Reif, Emily, Minsuk Kahng, & Savvas Petridis. (2023). Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models. 236–240. 6 indexed citations

Ippolito, Daphne, Liam Dugan, Emily Reif, et al.. (2022). The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank. 2421–2432. 2 indexed citations

Yuan, Ann, Andy Coenen, Emily Reif, & Daphne Ippolito. (2022). Wordcraft: Story Writing With Large Language Models. 841–852. 174 indexed citations breakdown →

10.

Kim, Been, Emily Reif, Martin Wattenberg, Samy Bengio, & Michael C. Mozer. (2021). Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure. Computational Brain & Behavior. 4(3). 251–263. 27 indexed citations

11.

Sánchez-Lengeling, Benjamín, et al.. (2021). A Gentle Introduction to Graph Neural Networks. 6(8). 143 indexed citations

12.

Sánchez-Lengeling, Benjamín, Jennifer N. Wei, Brian K. Lee, et al.. (2020). Evaluating Attribution for Graph Neural Networks. Neural Information Processing Systems. 33. 5898–5910. 31 indexed citations

13.

Tenney, Ian, James Wexler, Jasmijn Bastings, et al.. (2020). The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. 107–118. 73 indexed citations

14.

Reif, Emily, Ann Yuan, Martin Wattenberg, et al.. (2019). Visualizing and Measuring the Geometry of BERT. Neural Information Processing Systems. 32. 8592–8600. 70 indexed citations

15.

Cai, Carrie J., Emily Reif, Narayan Hegde, et al.. (2019). Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. 1–14. 222 indexed citations

16.

Kim, Been, Emily Reif, Martin Wattenberg, & Samy Bengio. (2019). Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure.. 17 indexed citations

17.

Abel, David, et al.. (2018). Bandit-Based Solar Panel Control. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 2 indexed citations

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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