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
- AI-based Problem Solving and Planning 1
- Natural Language Processing Techniques 1
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- Web Data Mining and Analysis 1
- Co-authors
- Jina Suh (1 shared paper)Kori Inkpen (1 shared paper)Eric Horvitz (1 shared paper)Saleema Amershi (1 shared paper)Adam Fourney (1 shared paper)Shamsi T. Iqbal (1 shared paper)Besmira Nushi (1 shared paper)Jaime Teevan (1 shared paper)
- Journals
- North American Chapter of the Association for Computational Linguistics (1 paper)
- Partner nations
- United StatesCanadaSpain
In The Last Decade
Dan Weld
6 papers receiving 1.2k citations
Dan Weld's Hit Papers
Peers
Comparison fields: 5 of 109
- Health Informatics 117
- Human-Computer Interaction 214
- Safety Research 271
- Computer Science Applications 109
- Artificial Intelligence 550
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 | 1112 |
| 2 | Foundations of Assisted Cognition Systems | 2003 | 55 |
| 3 | 2009 | 39 | |
| 4 | 2019 | 31 | |
| 5 | Machine Reading at the University of Washington | 2010 | 23 |
| 6 | Planning and knowledge representation for softbots | 1997 | 10 |
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 1.3k indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (2 papers), Web Data Mining and Analysis (1 paper), Data Management and Algorithms (1 paper), AI-based Problem Solving and Planning (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper), Natural Language Processing Techniques (1 paper), Persona Design and Applications (1 paper) and Innovative Human-Technology Interaction (1 paper). The work is most often cited by research in Health Informatics (117 citations), Human-Computer Interaction (214 citations), Safety Research (271 citations), Computer Science Applications (109 citations) and Artificial Intelligence (550 citations). Dan Weld has collaborated with scholars based in United States, Canada and Spain. Frequent co-authors include Jina Suh, Kori Inkpen, Eric Horvitz, Saleema Amershi, Adam Fourney, Shamsi T. Iqbal, Besmira Nushi, Jaime Teevan, Mihaela Vorvoreanu and Paul N. Bennett. 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.