Xingguo Li
- Computational Mathematics top 10%
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques 15
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- Stochastic Gradient Optimization Techniques 5
- Anomaly Detection Techniques and Applications 3
- Machine Learning and Algorithms 2
- Adversarial Robustness in Machine Learning 2
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- Distributed Sensor Networks and Detection Algorithms 4
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- Structural Health Monitoring Techniques 3
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- Optical measurement and interference techniques 3
Xingguo Li
32 papers receiving 249 citations
Peers
Comparison fields: 5 of 62
- Computational Mathematics 9
- Metals and Alloys 14
- Computational Mechanics 104
- Acoustics and Ultrasonics 3
- Signal Processing 27
Countries citing papers authored by Xingguo Li
This map shows the geographic impact of Xingguo Li'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 Xingguo Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xingguo Li more than expected).
Fields of papers citing papers by Xingguo Li
This network shows the impact of papers produced by Xingguo Li. 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 Xingguo Li. The network helps show where Xingguo Li may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xingguo Li, 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 | 2023 | 7 | |
| 2 | 2023 | 11 | |
| 3 | 2022 | 6 | |
| 4 | 2020 | 5 | |
| 5 | Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality | 2020 | 1 |
| 6 | A dictionary-based generalization of robust PCA Part I: Study of theoretical properties | 2019 | 1 |
| 7 | On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition | 2019 | 3 |
| 8 | 2019 | 2 | |
| 9 | Towards Black-box Iterative Machine Teaching. | 2018 | 4 |
| 10 | On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning | 2017 | 6 |
| 11 | 2017 | 3 | |
| 12 | 2016 | 9 | |
| 13 | 2016 | 2 | |
| 14 | 2015 | 3 | |
| 15 | 2015 | 4 | |
| 16 | Case-Based Reasoning ISP Knowledge Reuse Method | 2010 | 4 |
| 17 | Ultrafast Thermoelasticity Effect of Gold Film Heated by Femtosecond Pulse Lasers | 2010 | 1 |
| 18 | 2005 | 1 | |
| 19 | 2005 | 6 | |
| 20 | 2001 | 35 |
About Xingguo Li
Xingguo Li is a scholar working on Computational Mathematics, Computational Mechanics and Artificial Intelligence, having authored 32 papers that have together received 261 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (15 papers), Stochastic Gradient Optimization Techniques (5 papers), Distributed Sensor Networks and Detection Algorithms (4 papers), Structural Health Monitoring Techniques (3 papers), Optical measurement and interference techniques (3 papers), Anomaly Detection Techniques and Applications (3 papers), Machine Learning and Algorithms (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Computational Mathematics (9 citations), Metals and Alloys (14 citations) and Computational Mechanics (104 citations). Xingguo Li has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jarvis Haupt, Linghui Wang, Fushun Liu, Tuo Zhao, S. Takahashi, Dianzi Liu, Honggui Li, Mingyi Hong, Tong Zhang and Sirisha Rambhatla. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and Journal of Materials Processing Technology.
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.