Jay M. Pittman
Impact in
- Human-Computer Interaction top 2%
- Usability and User Interface Design
- Interactive and Immersive Displays
- Hand Gesture Recognition Systems
- Artificial Intelligence top 5%
- Speech and dialogue systems
- Multi-Agent Systems and Negotiation
- Natural Language Processing Techniques
Papers in
-
- Speech and dialogue systems 4
- Multi-Agent Systems and Negotiation 3
-
- Alzheimer's disease research and treatments 4
- Co-authors
- Philip R. Cohen (4 shared papers)David R. McGee (4 shared papers)Ira Smith (4 shared papers)Michael Johnston (4 shared papers)Sharon Oviatt (3 shared papers)Liang Chen (1 shared paper)Patrick C. Moore (4 shared papers)Joseph R. Sachleben (4 shared papers)
- Journals
- Protein Science (2 papers)Journal of Pragmatics (1 paper)Academic Radiology (1 paper)Protein Expression and Purification (1 paper)Biochemistry (1 paper)
- Partner nations
- United StatesItaly
In The Last Decade
Jay M. Pittman
11 papers receiving 389 citations
Peers
Comparison fields: 5 of 68
- Human-Computer Interaction 164
- Artificial Intelligence 308
- Computer Vision and Pattern Recognition 68
- Cognitive Neuroscience 45
- Social Psychology 48
Countries citing papers authored by Jay M. Pittman
This map shows the geographic impact of Jay M. Pittman'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 Jay M. Pittman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay M. Pittman more than expected).
Fields of papers citing papers by Jay M. Pittman
This network shows the impact of papers produced by Jay M. Pittman. 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 Jay M. Pittman. The network helps show where Jay M. Pittman may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay M. Pittman, 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 | 1997 | 308 | |
| 2 | 2019 | 64 | |
| 3 | 1997 | 36 | |
| 4 | 1997 | 20 | |
| 5 | QuickSet: A Multimodal Interface for Distributed Interactive Simulation | 2003 | 7 |
| 6 | 2022 | 6 | |
| 7 | 2019 | 4 | |
| 8 | 2021 | 4 | |
| 9 | 2021 | 2 | |
| 10 | 2019 | 2 | |
| 11 | 2021 | 1 |
About Jay M. Pittman
Jay M. Pittman is a scholar working on Artificial Intelligence, Physiology, Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 11 papers that have together received 454 indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (4 papers), Speech and dialogue systems (4 papers), Multi-Agent Systems and Negotiation (3 papers), MRI in cancer diagnosis (2 papers), Protein Structure and Dynamics (2 papers), Language, Discourse, Communication Strategies (1 paper), Context-Aware Activity Recognition Systems (1 paper) and Peptidase Inhibition and Analysis (1 paper). The work is most often cited by research in Human-Computer Interaction (164 citations), Artificial Intelligence (308 citations), Computer Vision and Pattern Recognition (68 citations), Cognitive Neuroscience (45 citations) and Social Psychology (48 citations). Jay M. Pittman has collaborated with scholars based in United States and Italy. Frequent co-authors include Philip R. Cohen, David R. McGee, Ira Smith, Michael Johnston, Sharon Oviatt, Liang Chen, Patrick C. Moore, Joseph R. Sachleben, Atul Srivastava and Jonathan Zerweck. Their work appears in journals such as Protein Science, Journal of Pragmatics, Academic Radiology, Protein Expression and Purification and Biochemistry.
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.