Daniel Cullina
Impact in
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
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- Graph Theory and Algorithms
Papers in
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- Advanced Graph Neural Networks 8
- Algorithms and Data Compression 4
- Privacy-Preserving Technologies in Data 4
- Adversarial Robustness in Machine Learning 3
- Anomaly Detection Techniques and Applications 3
- Co-authors
- Negar Kiyavash (16 shared papers)Prateek Mittal (6 shared papers)Arjun Nitin Bhagoji (3 shared papers)Tracey Ho (1 shared paper)Alexandros G. Dimakis (1 shared paper)Chawin Sitawarin (2 shared papers)Matthias Grossglauser (3 shared papers)H. Vincent Poor (3 shared papers)
- Journals
- ACM SIGMETRICS Performance Evaluation Review (3 papers)IEEE Transactions on Information Theory (3 papers)Real-Time Systems (1 paper)Proceedings of the ACM on Measurement and Analysis of Computing Systems (2 papers)arXiv (Cornell University) (4 papers)
- Partner nations
- United StatesSwitzerlandItaly
In The Last Decade
Daniel Cullina
23 papers receiving 319 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 212
- Computer Vision and Pattern Recognition 79
- Computer Networks and Communications 83
- Computational Theory and Mathematics 53
- Signal Processing 35
Countries citing papers authored by Daniel Cullina
This map shows the geographic impact of Daniel Cullina'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 Cullina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Cullina more than expected).
Fields of papers citing papers by Daniel Cullina
This network shows the impact of papers produced by Daniel Cullina. 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 Cullina. The network helps show where Daniel Cullina may publish in the future.
Co-authors
The 18 scholars most cited alongside Daniel Cullina, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 44 | |
| 2 | 2016 | 41 | |
| 3 | 2016 | 39 | |
| 4 | 2019 | 36 | |
| 5 | 2014 | 25 | |
| 6 | 2019 | 21 | |
| 7 | 2018 | 21 | |
| 8 | PAC-learning in the presence of adversaries | 2018 | 17 |
| 9 | 2019 | 13 | |
| 10 | 2012 | 12 | |
| 11 | 2019 | 10 | |
| 12 | 2016 | 10 | |
| 13 | 2020 | 8 | |
| 14 | 2016 | 6 | |
| 15 | 2013 | 6 | |
| 16 | 2020 | 6 | |
| 17 | 2020 | 6 | |
| 18 | 2019 | 4 | |
| 19 | 2018 | 4 | |
| 20 | 2014 | 3 |
About Daniel Cullina
Daniel Cullina is a scholar working on Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Industrial and Manufacturing Engineering and Computational Theory and Mathematics, having authored 25 papers that have together received 339 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), DNA and Biological Computing (7 papers), Advanced biosensing and bioanalysis techniques (5 papers), Algorithms and Data Compression (4 papers), Privacy-Preserving Technologies in Data (4 papers), Real-Time Systems Scheduling (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Artificial Intelligence (212 citations), Computer Vision and Pattern Recognition (79 citations), Computer Networks and Communications (83 citations), Computational Theory and Mathematics (53 citations) and Signal Processing (35 citations). Daniel Cullina has collaborated with scholars based in United States, Switzerland and Italy. Frequent co-authors include Negar Kiyavash, Prateek Mittal, Arjun Nitin Bhagoji, Tracey Ho, Alexandros G. Dimakis, Chawin Sitawarin, Matthias Grossglauser, H. Vincent Poor, Ankur A. Kulkarni and Liwei Song. Their work appears in journals such as ACM SIGMETRICS Performance Evaluation Review, IEEE Transactions on Information Theory, Real-Time Systems, Proceedings of the ACM on Measurement and Analysis of Computing Systems 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.