Dayeol Lee
Impact in
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
- Embedded Systems Design Techniques
- Physical Unclonable Functions (PUFs) and Hardware Security
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in
-
- Cloud Data Security Solutions 3
- Cloud Computing and Resource Management 2
-
- Security and Verification in Computing 5
- Co-authors
- Krste Asanović (7 shared papers)Dawn Song (5 shared papers)David Kohlbrenner (4 shared papers)Shweta Shinde (3 shared papers)Alon Amid (2 shared papers)David Biancolin (2 shared papers)Donggyu Kim (2 shared papers)Emmanuel Amaro (2 shared papers)
- Journals
- IEEE Security & Privacy (1 paper)IEEE Micro (1 paper)UC Berkeley (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Dayeol Lee
8 papers receiving 440 citations
Dayeol Lee's Hit Papers
Peers
Comparison fields: 5 of 33
- Hardware and Architecture 197
- Signal Processing 94
- Artificial Intelligence 258
- Computer Networks and Communications 151
- Information Systems 138
Countries citing papers authored by Dayeol Lee
This map shows the geographic impact of Dayeol Lee'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 Dayeol Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dayeol Lee more than expected).
Fields of papers citing papers by Dayeol Lee
This network shows the impact of papers produced by Dayeol Lee. 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 Dayeol Lee. The network helps show where Dayeol Lee may publish in the future.
Co-authors
The 25 scholars most cited alongside Dayeol Lee, 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 | Keystone Hit paper breakdown → | 2020 | 237 |
| 2 | 2018 | 156 | |
| 3 | 2018 | 23 | |
| 4 | Keystone: A Framework for Architecting TEEs. | 2019 | 16 |
| 5 | 2019 | 7 | |
| 6 | 2019 | 6 | |
| 7 | 2022 | 4 | |
| 8 | 2020 | 4 |
About Dayeol Lee
Dayeol Lee is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Electrical and Electronic Engineering, having authored 8 papers that have together received 453 indexed citations. Recurring topics across this work include Security and Verification in Computing (5 papers), Cloud Data Security Solutions (3 papers), Advanced Malware Detection Techniques (3 papers), Cloud Computing and Resource Management (2 papers), Software-Defined Networks and 5G (2 papers), Distributed systems and fault tolerance (2 papers), Interconnection Networks and Systems (2 papers) and Advanced Memory and Neural Computing (2 papers). The work is most often cited by research in Hardware and Architecture (197 citations), Signal Processing (94 citations), Artificial Intelligence (258 citations), Computer Networks and Communications (151 citations) and Information Systems (138 citations). Dayeol Lee has collaborated with scholars based in United States and South Korea. Frequent co-authors include Krste Asanović, Dawn Song, David Kohlbrenner, Shweta Shinde, Alon Amid, David Biancolin, Donggyu Kim, Emmanuel Amaro, Qijing Huang and Kyle D. Kovacs. Their work appears in journals such as IEEE Security & Privacy, IEEE Micro, UC Berkeley, arXiv (Cornell University) and Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security.
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.