Eli Lifland
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Natural Language Processing Techniques
- Hate Speech and Cyberbullying Detection
- Anomaly Detection Techniques and Applications
- Explainable Artificial Intelligence (XAI)
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in
-
- Topic Modeling 3
- Adversarial Robustness in Machine Learning 3
- Semantic Web and Ontologies 1
- Natural Language Processing Techniques 1
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- Advanced Malware Detection Techniques 2
- Data Management and Algorithms 1
- Co-authors
- Yanjun Qi (3 shared papers)John X. Morris (3 shared papers)Jin Yong Yoo (3 shared papers)Jake Grigsby (1 shared paper)Di Jin (1 shared paper)Meiyi Ma (1 shared paper)Ezio Bartocci (1 shared paper)John A. Stankovic (1 shared paper)
- Journals
- IEEE Internet of Things Journal (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesAustria
In The Last Decade
Eli Lifland
4 papers receiving 375 citations
Eli Lifland's Hit Papers
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 334
- Signal Processing 83
- Software 15
- Health Informatics 5
- Information Systems 51
Countries citing papers authored by Eli Lifland
This map shows the geographic impact of Eli Lifland'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 Eli Lifland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eli Lifland more than expected).
Fields of papers citing papers by Eli Lifland
This network shows the impact of papers produced by Eli Lifland. 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 Eli Lifland. The network helps show where Eli Lifland may publish in the future.
Co-authors
The 9 scholars most cited alongside Eli Lifland, 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 | TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP Hit paper breakdown → | 2020 | 315 |
| 2 | 2021 | 25 | |
| 3 | TextAttack: A Framework for Adversarial Attacks in Natural Language Processing | 2020 | 24 |
| 4 | 2020 | 24 |
About Eli Lifland
Eli Lifland is a scholar working on Artificial Intelligence, Signal Processing, Computer Networks and Communications, Infectious Diseases and Organic Chemistry, having authored 4 papers that have together received 388 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Adversarial Robustness in Machine Learning (3 papers), Advanced Malware Detection Techniques (2 papers), Semantic Web and Ontologies (1 paper), Data Management and Algorithms (1 paper), Natural Language Processing Techniques (1 paper) and Advanced Database Systems and Queries (1 paper). The work is most often cited by research in Artificial Intelligence (334 citations), Signal Processing (83 citations), Software (15 citations), Health Informatics (5 citations) and Information Systems (51 citations). Eli Lifland has collaborated with scholars based in United States and Austria. Frequent co-authors include Yanjun Qi, John X. Morris, Jin Yong Yoo, Jake Grigsby, Di Jin, Meiyi Ma, Ezio Bartocci, John A. Stankovic and Lu Feng. Their work appears in journals such as IEEE Internet of Things Journal 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.