Dan Weld

1.9k citations
6 papers · 973 · 1 hit paper · h-index 6

Impact in

Papers in

Dan Weld

6 papers receiving 933 citations

Hit Papers

Guidelines for Human-AI Interaction 2019 · 841 citations
8410+2+4Years since publication250500750

Peers

Dan Weld
Comparison fields: 5 of 105
  • Health Informatics 96
  • Human-Computer Interaction 172
  • Safety Research 223
  • Computer Science Applications 97
  • Artificial Intelligence 450
Replace Justin D. Weisz with:
Justin D. Weisz United States
Adam Fourney United States
Ashraf Abdul Singapore
Todd Kulesza United States
Danding Wang China
Chad C. Tossell United States
Malin Eiband Germany
Tongshuang Wu United States
Stephanie Houde United States
Nikola Banović United States
Dan Weld relative to Justin D. Weisz United States Justin D. Weisz's profile →
Citations per field
00.5×1.7×
Justin D. Weisz · 1×
Citations per year

Countries citing papers authored by Dan Weld

Since Specialization
Citations

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

Fields of papers citing papers by Dan Weld

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dan Weld, 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 Dan Weld Line = papers co-authored together Dan Weld links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1
Guidelines for Human-AI Interaction
Hit paper breakdown →
2019841
2
Foundations of Assisted Cognition Systems
200346
3 200930
4 201929
5
Machine Reading at the University of Washington
201022
6
Planning and knowledge representation for softbots
19975

About Dan Weld

Dan Weld is a scholar working on Artificial Intelligence, Information Systems, Social Psychology, Computer Vision and Pattern Recognition and Signal Processing, having authored 6 papers that have together received 973 indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (2 papers), Data Management and Algorithms (1 paper), Natural Language Processing Techniques (1 paper), Scheduling and Optimization Algorithms (1 paper), Software Engineering Research (1 paper), Innovative Human-Technology Interaction (1 paper), Persona Design and Applications (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Health Informatics (96 citations), Human-Computer Interaction (172 citations), Safety Research (223 citations), Computer Science Applications (97 citations) and Artificial Intelligence (450 citations). Dan Weld has collaborated with scholars based in United States, Spain and Canada. Frequent co-authors include Paul N. Bennett, Jaime Teevan, Shamsi T. Iqbal, Eric Horvitz, Jina Suh, Kori Inkpen, Adam Fourney, Mihaela Vorvoreanu, Besmira Nushi and Saleema Amershi. Their work appears in journals such as North American Chapter of the Association for Computational Linguistics.

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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