Emily Reif
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Health Informatics top 1%
- Information Systems top 10%
- Safety Research top 5%
- Co-authors
- Ann YuanAndy CoenenAdam PearceDaphne IppolitoMartin WattenbergBeen KimBenjamín Sánchez-LengelingFernanda Viégas
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (6 papers)Software Engineering Research (3 papers)
- Journals
- IEEE Transactions on Visualization and Computer GraphicsComputational Brain & BehaviorNeural Information Processing Systems
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Emily Reif
16 papers receiving 772 citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 456
- Computer Vision and Pattern Recognition 110
- Health Informatics 91
- Information Systems 74
- Safety Research 72
Countries citing papers authored by Emily Reif
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 13 | |
| 3 | 11 | |
| 4 | 7 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 2 | |
| 9 | Wordcraft: Story Writing With Large Language Modelsbreakdown → | 174 |
| 10 | 27 | |
| 11 | 143 | |
| 12 | Evaluating Attribution for Graph Neural Networks | 31 |
| 13 | 73 | |
| 14 | Visualizing and Measuring the Geometry of BERT | 70 |
| 15 | 222 | |
| 16 | Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. | 17 |
| 17 | 2 |
About Emily Reif
Emily Reif is a scholar working on Artificial Intelligence, General Social Sciences and Information Systems, having authored 17 papers that have together received 802 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Software Engineering Research (3 papers). The work is most often cited by research in Health Informatics (91 citations), Artificial Intelligence (456 citations) and Safety Research (72 citations). Emily Reif has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ann Yuan, Andy Coenen, Adam Pearce, Daphne Ippolito, Martin Wattenberg, Been Kim, Benjamín Sánchez-Lengeling, Fernanda Viégas, Michael Terry and Martin C. Stumpe. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computational Brain & Behavior and Neural Information Processing Systems.
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.