Dan Weld
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
- Human-Computer Interaction top 2%
- Innovative Human-Technology Interaction
Papers in
-
- Multi-Agent Systems and Negotiation 2
- Natural Language Processing Techniques 2
- Topic Modeling 2
- AI-based Problem Solving and Planning 1
- Bayesian Modeling and Causal Inference 1
- Co-authors
- Kori M. Inkpen (1 shared paper)Eric Horvitz (1 shared paper)Besmira Nushi (1 shared paper)Shamsi T. Iqbal (1 shared paper)Adam Fourney (1 shared paper)Paul Nathan Bennett (1 shared paper)Mihaela Vorvoreanu (1 shared paper)Saleema Amershi (1 shared paper)
- Journals
- North American Chapter of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Dan Weld
6 papers receiving 1.4k citations
Dan Weld's Hit Papers
Peers
Comparison fields: 5 of 106
- Health Informatics 131
- Human-Computer Interaction 226
- Safety Research 302
- Computer Science Applications 123
- Artificial Intelligence 667
Countries citing papers authored by Dan Weld
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
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.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Guidelines for Human-AI Interaction Hit paper breakdown → | 2019 | 1293 |
| 2 | 2019 | 81 | |
| 3 | Foundations of Assisted Cognition Systems | 2003 | 54 |
| 4 | 2019 | 33 | |
| 5 | Machine Reading at the University of Washington | 2010 | 25 |
| 6 | Planning and knowledge representation for softbots | 1997 | 10 |
About Dan Weld
Dan Weld is a scholar working on Artificial Intelligence, Computer Science Applications, Human-Computer Interaction, Information Systems and Industrial and Manufacturing Engineering, having authored 6 papers that have together received 1.5k indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (2 papers), Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers), Context-Aware Activity Recognition Systems (1 paper), Software Engineering Research (1 paper), AI-based Problem Solving and Planning (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Innovative Human-Technology Interaction (1 paper). The work is most often cited by research in Health Informatics (131 citations), Human-Computer Interaction (226 citations), Safety Research (302 citations), Computer Science Applications (123 citations) and Artificial Intelligence (667 citations). Dan Weld has collaborated with scholars based in United States. Frequent co-authors include Kori M. Inkpen, Eric Horvitz, Besmira Nushi, Shamsi T. Iqbal, Adam Fourney, Paul Nathan Bennett, Mihaela Vorvoreanu, Saleema Amershi, Jaime B. Teevan and Jina Suh. Their work appears in journals such as North American Chapter of the Association for Computational Linguistics 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.