Cong Liao
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
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- Advanced Malware Detection Techniques
Papers in
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- Spam and Phishing Detection 3
- Cloud Data Security Solutions 2
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- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 2
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Anna Squicciarini (8 shared papers)Sencun Zhu (2 shared papers)Haoti Zhong (2 shared papers)David J. Miller (1 shared paper)Krishna K. Venkatasubramanian (1 shared paper)Jian Chang (1 shared paper)Insup Lee (1 shared paper)Christopher Griffin (3 shared papers)
- Journals
- IEEE Transactions on Dependable and Secure Computing (1 paper)Neurocomputing (1 paper)Social Network Analysis and Mining (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Cong Liao
11 papers receiving 165 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 117
- Signal Processing 38
- Computer Networks and Communications 47
- Information Systems 38
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Cong Liao
This map shows the geographic impact of Cong Liao'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 Cong Liao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cong Liao more than expected).
Fields of papers citing papers by Cong Liao
This network shows the impact of papers produced by Cong Liao. 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 Cong Liao. The network helps show where Cong Liao may publish in the future.
Co-authors
The 14 scholars most cited alongside Cong Liao, 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 | 97 | |
| 2 | 2013 | 33 | |
| 3 | 2015 | 14 | |
| 4 | 2018 | 7 | |
| 5 | 2020 | 6 | |
| 6 | 2016 | 4 | |
| 7 | 2018 | 4 | |
| 8 | 2016 | 3 | |
| 9 | 2015 | 2 | |
| 10 | IMPLEMENTATION OF THE EPICS DATA ARCHIVE SYSTEM FOR THE TPS PROJECT | 2013 | 2 |
| 11 | 2015 | 1 | |
| 12 | 2025 | 0 |
About Cong Liao
Cong Liao is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering and Statistical and Nonlinear Physics, having authored 12 papers that have together received 173 indexed citations. Recurring topics across this work include Spam and Phishing Detection (3 papers), Complex Network Analysis Techniques (2 papers), Opinion Dynamics and Social Influence (2 papers), Privacy-Preserving Technologies in Data (2 papers), Cloud Data Security Solutions (2 papers), Cryptography and Data Security (2 papers), Network Security and Intrusion Detection (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Artificial Intelligence (117 citations), Signal Processing (38 citations), Computer Networks and Communications (47 citations), Information Systems (38 citations) and Computer Vision and Pattern Recognition (32 citations). Cong Liao has collaborated with scholars based in United States and China. Frequent co-authors include Anna Squicciarini, Sencun Zhu, Haoti Zhong, David J. Miller, Krishna K. Venkatasubramanian, Jian Chang, Insup Lee, Christopher Griffin, Dan Lin and Sarah Rajtmajer. Their work appears in journals such as IEEE Transactions on Dependable and Secure Computing, Neurocomputing and Social Network Analysis and Mining.
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.