Ivan Evtimov
Impact in
- Artificial Intelligence top 1%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Signal Processing top 2%
- Advanced Malware Detection Techniques
Papers in
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- Adversarial Robustness in Machine Learning 2
- Privacy-Preserving Technologies in Data 1
- Internet Traffic Analysis and Secure E-voting 1
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- Network Security and Intrusion Detection 1
- Co-authors
- Tadayoshi Kohno (4 shared papers)Earlence Fernandes (3 shared papers)Dawn Song (1 shared paper)Amir Rahmati (2 shared papers)Bo Li (1 shared paper)Chaowei Xiao (1 shared paper)Atul Prakash (2 shared papers)Kevin Eykholt (2 shared papers)
- Journals
- Berkeley technology law journal (1 paper)SSRN Electronic Journal (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Ivan Evtimov
5 papers receiving 1.2k citations
Ivan Evtimov's Hit Papers
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 1.0k
- Signal Processing 321
- Hardware and Architecture 122
- Health Informatics 18
- Computer Vision and Pattern Recognition 253
Countries citing papers authored by Ivan Evtimov
This map shows the geographic impact of Ivan Evtimov'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 Ivan Evtimov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Evtimov more than expected).
Fields of papers citing papers by Ivan Evtimov
This network shows the impact of papers produced by Ivan Evtimov. 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 Ivan Evtimov. The network helps show where Ivan Evtimov may publish in the future.
Co-authors
The 16 scholars most cited alongside Ivan Evtimov, 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 | Robust Physical-World Attacks on Deep Learning Visual Classification Hit paper breakdown → | 2018 | 1193 |
| 2 | 2021 | 8 | |
| 3 | 2018 | 8 | |
| 4 | Tools for Active and Passive Network Side-Channel Detection for Web Applications | 2018 | 6 |
| 5 | 2019 | 1 |
About Ivan Evtimov
Ivan Evtimov is a scholar working on Artificial Intelligence, Computer Networks and Communications, Molecular Biology, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 1.2k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Privacy-Preserving Technologies in Data (1 paper), Integrated Circuits and Semiconductor Failure Analysis (1 paper), Face recognition and analysis (1 paper), Internet Traffic Analysis and Secure E-voting (1 paper), Bacillus and Francisella bacterial research (1 paper), Robot Manipulation and Learning (1 paper) and Network Security and Intrusion Detection (1 paper). The work is most often cited by research in Artificial Intelligence (1.0k citations), Signal Processing (321 citations), Hardware and Architecture (122 citations), Health Informatics (18 citations) and Computer Vision and Pattern Recognition (253 citations). Ivan Evtimov has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Tadayoshi Kohno, Earlence Fernandes, Dawn Song, Amir Rahmati, Bo Li, Chaowei Xiao, Atul Prakash, Kevin Eykholt, Pascal Sturmfels and Ryan Calo. Their work appears in journals such as Berkeley technology law journal, SSRN Electronic Journal and DOAJ (DOAJ: Directory of Open Access Journals).
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.