Eugene Bagdasaryan
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Networks and Communications top 10%
- Signal Processing
- Computer Vision and Pattern Recognition
- Co-authors
- Vitaly ShmatikovDeborah EstrinAndreas VeitYiqing HuaLongqi YangRobbert van RenesseZhen SunZhiming Shen
- Topics
- Topic Modeling (3 papers)Adversarial Robustness in Machine Learning (3 papers)Privacy-Preserving Technologies in Data (2 papers)
- Journals
- arXiv (Cornell University)International Conference on Artificial Intelligence and StatisticsProceedings on Privacy Enhancing Technologies
- Partner nations
- United States
In The Last Decade
Eugene Bagdasaryan
8 papers receiving 230 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 168
- Information Systems 89
- Computer Networks and Communications 73
- Signal Processing 40
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Eugene Bagdasaryan
This map shows the geographic impact of Eugene Bagdasaryan'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 Eugene Bagdasaryan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Bagdasaryan more than expected).
Fields of papers citing papers by Eugene Bagdasaryan
This network shows the impact of papers produced by Eugene Bagdasaryan. 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 Eugene Bagdasaryan. The network helps show where Eugene Bagdasaryan may publish in the future.
Co-authorship network of co-authors of Eugene Bagdasaryan
This figure shows the co-authorship network connecting the top 25 collaborators of Eugene Bagdasaryan. A scholar is included among the top collaborators of Eugene Bagdasaryan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eugene Bagdasaryan. Eugene Bagdasaryan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 13 | |
| 3 | Blind Backdoors in Deep Learning Models | 51 |
| 4 | 7 | |
| 5 | 51 | |
| 6 | 32 | |
| 7 | How To Backdoor Federated Learning. | 56 |
| 8 | 1 |
About Eugene Bagdasaryan
Eugene Bagdasaryan is a scholar working on Artificial Intelligence, Computer Science Applications and Management Science and Operations Research, having authored 8 papers that have together received 235 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Privacy-Preserving Technologies in Data (2 papers). The work is most often cited by research in Artificial Intelligence (168 citations), Information Systems (89 citations) and Signal Processing (40 citations). Eugene Bagdasaryan has collaborated with scholars based in United States. Frequent co-authors include Vitaly Shmatikov, Deborah Estrin, Andreas Veit, Yiqing Hua, Longqi Yang, Robbert van Renesse, Zhen Sun, Zhiming Shen, Christina Delimitrou and Hakim Weatherspoon. Their work appears in journals such as arXiv (Cornell University), International Conference on Artificial Intelligence and Statistics and Proceedings on Privacy Enhancing Technologies.
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.