Ethan Fast

1.7k citations
17 papers · 751 · 1 hit paper · h-index 10

Impact in

Papers in

Ethan Fast

17 papers receiving 719 citations

Hit Papers

Long-Term Trends in the Public Perception of Artificial Intelligence 2017 · 253 citations
2530+3+6Years since publication50100150200250

Peers

Ethan Fast
Comparison fields: 5 of 91
  • Health Informatics 44
  • Software 95
  • Computer Science Applications 82
  • Safety Research 91
  • Artificial Intelligence 222
Replace Alireza Ahadi with:
Alireza Ahadi Australia
Amy X. Zhang United States
Susan Haller United States
Erik T. Mueller United States
Gary K. W. Wong Hong Kong
David G. Robinson United States
John Zeleznikow Australia
Andrew Smart United States
Jeehyung Lee United States
Mark Simkin United States
Ethan Fast relative to Alireza Ahadi Australia Alireza Ahadi's profile →
Citations per field
00.5×8.8×
Alireza Ahadi · 1×
Citations per year

Countries citing papers authored by Ethan Fast

Since Specialization
Citations

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

Fields of papers citing papers by Ethan Fast

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ethan Fast, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ethan Fast Line = papers co-authored together Ethan Fast links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Long-Term Trends in the Public Perception of Artificial Intelligence
Hit paper breakdown →
2017253
2 2019209
3 201865
4 201357
5 201046
6 202029
7 201424
8 201313
9 201612
10 201611
11 20178
12 20167
13 20216
14 20184
15 20173
16
Software Mutational Robustness: Bridging The Gap Between Mutation Testing and Evolutionary Biology
20122
17 20152

About Ethan Fast

Ethan Fast is a scholar working on Artificial Intelligence, Information Systems, Computer Science Applications, Software and Molecular Biology, having authored 17 papers that have together received 751 indexed citations. Recurring topics across this work include Software Engineering Research (6 papers), Online Learning and Analytics (3 papers), Software Testing and Debugging Techniques (3 papers), Intelligent Tutoring Systems and Adaptive Learning (3 papers), Topic Modeling (3 papers), Sentiment Analysis and Opinion Mining (2 papers), Software Reliability and Analysis Research (2 papers) and Misinformation and Its Impacts (2 papers). The work is most often cited by research in Health Informatics (44 citations), Software (95 citations), Computer Science Applications (82 citations), Safety Research (91 citations) and Artificial Intelligence (222 citations). Ethan Fast has collaborated with scholars based in United States, United Kingdom and Liechtenstein. Frequent co-authors include Eric Horvitz, Michael S. Bernstein, Binbin Chen, Westley Weimer, Stephanie Forrest, Ronald Levy, Chih Long Liu, Lisa E. Wagar, Russ B. Altman and Yagmur Muftuoglu. Their work appears in journals such as Genetic Programming and Evolvable Machines, Nature Biotechnology, Blood, Proceedings of the International AAAI Conference on Web and Social Media and arXiv (Cornell University).

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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