Jeff Pasternack
Impact in
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- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 5%
- Topic Modeling
- Data Stream Mining Techniques
Papers in
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- Topic Modeling 4
- Natural Language Processing Techniques 2
- Data Stream Mining Techniques 1
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- Web Data Mining and Analysis 2
- Spam and Phishing Detection 1
- Co-authors
- Dan Roth (7 shared papers)Jiawei Han (2 shared papers)Tarek Abdelzaher (2 shared papers)Omid Fatemieh (1 shared paper)Charų C. Aggarwal (1 shared paper)Hossein Ahmadi (1 shared paper)Hieu Le (1 shared paper)Dong Wang (1 shared paper)
- Journals
- International Conference on Information Fusion (1 paper)International Conference on Computational Linguistics (1 paper)Information Processing in Sensor Networks (1 paper)
- Partner nations
- United States
In The Last Decade
Jeff Pasternack
7 papers receiving 400 citations
Peers
Comparison fields: 5 of 41
- Computer Science Applications 202
- Artificial Intelligence 279
- Transportation 50
- Information Systems 152
- Management Science and Operations Research 80
Countries citing papers authored by Jeff Pasternack
This map shows the geographic impact of Jeff Pasternack'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 Jeff Pasternack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Pasternack more than expected).
Fields of papers citing papers by Jeff Pasternack
This network shows the impact of papers produced by Jeff Pasternack. 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 Jeff Pasternack. The network helps show where Jeff Pasternack may publish in the future.
Co-authors
The 13 scholars most cited alongside Jeff Pasternack, 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 | Knowing What to Believe (when you already know something) | 2010 | 173 |
| 2 | 2013 | 106 | |
| 3 | 2009 | 52 | |
| 4 | On Bayesian interpretation of fact-finding in information networks | 2011 | 39 |
| 5 | 2011 | 23 | |
| 6 | Apollo: Towards factfinding in participatory sensing | 2011 | 12 |
| 7 | 2009 | 10 |
About Jeff Pasternack
Jeff Pasternack is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 7 papers that have together received 415 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (2 papers), Web Data Mining and Analysis (2 papers), Complex Network Analysis Techniques (1 paper), Data Stream Mining Techniques (1 paper), Spam and Phishing Detection (1 paper), Advanced Malware Detection Techniques (1 paper) and Handwritten Text Recognition Techniques (1 paper). The work is most often cited by research in Computer Science Applications (202 citations), Artificial Intelligence (279 citations), Transportation (50 citations), Information Systems (152 citations) and Management Science and Operations Research (80 citations). Jeff Pasternack has collaborated with scholars based in United States. Frequent co-authors include Dan Roth, Jiawei Han, Tarek Abdelzaher, Omid Fatemieh, Charų C. Aggarwal, Hossein Ahmadi, Hieu Le, Dong Wang, Yanbin Sun and Hieu Khac Le. Their work appears in journals such as International Conference on Information Fusion, International Conference on Computational Linguistics and Information Processing in Sensor Networks.
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.