Daniel Whitenack
Impact in
- Artificial Intelligence top 10%
- Natural Language Processing Techniques
- Topic Modeling
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- Scientific Computing and Data Management
Papers in
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- Advanced Chemical Physics Studies 4
- Atomic and Molecular Physics 2
- Cold Atom Physics and Bose-Einstein Condensates 1
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- Speech Recognition and Synthesis 3
- Topic Modeling 2
- Natural Language Processing Techniques 2
- Co-authors
- Adam Wasserman (3 shared papers)Yong P. Chen (1 shared paper)Payam Emami Khoonsari (1 shared paper)Marco Capuccini (1 shared paper)Michael R. Mack (1 shared paper)Stephanie Herman (1 shared paper)Joachim Burman (1 shared paper)Kim Kultima (1 shared paper)
- Journals
- Physical Review A (2 papers)The Journal of Physical Chemistry Letters (1 paper)Physical Review Letters (1 paper)Bioinformatics (1 paper)Chemical Physics Letters (1 paper)
- Partner nations
- United StatesGermanySweden
In The Last Decade
Daniel Whitenack
13 papers receiving 243 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 100
- Information Systems and Management 24
- Atomic and Molecular Physics, and Optics 75
- Safety Research 22
- Health Informatics 2
Countries citing papers authored by Daniel Whitenack
This map shows the geographic impact of Daniel Whitenack'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 Daniel Whitenack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Whitenack more than expected).
Fields of papers citing papers by Daniel Whitenack
This network shows the impact of papers produced by Daniel Whitenack. 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 Daniel Whitenack. The network helps show where Daniel Whitenack may publish in the future.
Co-authors
The 23 scholars most cited alongside Daniel Whitenack, 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 | 2020 | 109 | |
| 2 | 2018 | 38 | |
| 3 | 2011 | 23 | |
| 4 | 2013 | 19 | |
| 5 | 2009 | 16 | |
| 6 | 2012 | 15 | |
| 7 | 2024 | 10 | |
| 8 | 2022 | 8 | |
| 9 | 2021 | 4 | |
| 10 | 2012 | 3 | |
| 11 | 2022 | 3 | |
| 12 | 2022 | 2 | |
| 13 | 2012 | 1 |
About Daniel Whitenack
Daniel Whitenack is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence, Information Systems and Management, Statistical and Nonlinear Physics and Mathematical Physics, having authored 13 papers that have together received 251 indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (4 papers), Speech Recognition and Synthesis (3 papers), Atomic and Molecular Physics (2 papers), Topic Modeling (2 papers), Natural Language Processing Techniques (2 papers), Cold Atom Physics and Bose-Einstein Condensates (1 paper), Artificial Intelligence in Law (1 paper) and Spectral Theory in Mathematical Physics (1 paper). The work is most often cited by research in Artificial Intelligence (100 citations), Information Systems and Management (24 citations), Atomic and Molecular Physics, and Optics (75 citations), Safety Research (22 citations) and Health Informatics (2 citations). Daniel Whitenack has collaborated with scholars based in United States, Germany and Sweden. Frequent co-authors include Adam Wasserman, Yong P. Chen, Payam Emami Khoonsari, Marco Capuccini, Michael R. Mack, Stephanie Herman, Joachim Burman, Kim Kultima, Ola Spjuth and Sabine Brunswicker. Their work appears in journals such as Physical Review A, The Journal of Physical Chemistry Letters, Physical Review Letters, Bioinformatics and Chemical Physics Letters.
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.