Jernej Kos
Impact in
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- Blockchain Technology Applications and Security
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- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
- Privacy-Preserving Technologies in Data
Papers in
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- Privacy-Preserving Technologies in Data 1
- Cryptography and Data Security 1
- Adversarial Robustness in Machine Learning 1
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- Opportunistic and Delay-Tolerant Networks 2
- Wireless Networks and Protocols 1
- Caching and Content Delivery 1
- Co-authors
- Dawn Song (2 shared papers)Raymond Cheng (1 shared paper)Andrew Miller (1 shared paper)Fan Zhang (1 shared paper)Noah M. Johnson (1 shared paper)Warren He (2 shared papers)Ari Juels (2 shared papers)Luka Čehovin Zajc (1 shared paper)
- Journals
- Computer Networks (2 papers)IEEE Security & Privacy (1 paper)International Conference on Learning Representations (1 paper)IEEE Conference Proceedings (1 paper)
- Partner nations
- United StatesSloveniaUnited Kingdom
In The Last Decade
Jernej Kos
5 papers receiving 36 citations
Peers
Comparison fields: 5 of 20
- Information Systems 23
- Artificial Intelligence 22
- Computer Science Applications 3
- Computer Networks and Communications 11
- Management Information Systems 3
Countries citing papers authored by Jernej Kos
This map shows the geographic impact of Jernej Kos'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 Jernej Kos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jernej Kos more than expected).
Fields of papers citing papers by Jernej Kos
This network shows the impact of papers produced by Jernej Kos. 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 Jernej Kos. The network helps show where Jernej Kos may publish in the future.
Co-authors
The 12 scholars most cited alongside Jernej Kos, 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 | 24 | |
| 2 | Delving into adversarial attacks on deep policies | 2017 | 8 |
| 3 | 2015 | 5 | |
| 4 | 2015 | 3 | |
| 5 | Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts | 2019 | 1 |
About Jernej Kos
Jernej Kos is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Health Informatics and Infectious Diseases, having authored 5 papers that have together received 41 indexed citations. Recurring topics across this work include Opportunistic and Delay-Tolerant Networks (2 papers), Blockchain Technology Applications and Security (2 papers), Privacy-Preserving Technologies in Data (1 paper), Cryptography and Data Security (1 paper), Wireless Networks and Protocols (1 paper), Caching and Content Delivery (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Information Systems (23 citations), Artificial Intelligence (22 citations), Computer Science Applications (3 citations), Computer Networks and Communications (11 citations) and Management Information Systems (3 citations). Jernej Kos has collaborated with scholars based in United States, Slovenia and United Kingdom. Frequent co-authors include Dawn Song, Raymond Cheng, Andrew Miller, Fan Zhang, Noah M. Johnson, Warren He, Ari Juels, Luka Čehovin Zajc, Mahdi Aiash and Jonathan Loo. Their work appears in journals such as Computer Networks, IEEE Security & Privacy, International Conference on Learning Representations and IEEE Conference Proceedings.
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.