Asel Sagingalieva
Impact in
- Artificial Intelligence top 10%
- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Neural Networks and Reservoir Computing
- Neural Networks and Applications
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- Spectroscopy Techniques in Biomedical and Chemical Research
Papers in
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- Quantum Computing Algorithms and Architecture 4
- Quantum Information and Cryptography 2
- Adversarial Robustness in Machine Learning 1
- Computational Physics and Python Applications 1
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- Spectroscopy Techniques in Biomedical and Chemical Research 2
- Co-authors
- Alexey Melnikov (7 shared papers)W. Somogyi (1 shared paper)Fábio Cavalli (2 shared papers)Deborah Bonazza (1 shared paper)Fabrizio Zanconati (1 shared paper)
- Journals
- Machine Learning Science and Technology (2 papers)IEEE Transactions on Quantum Engineering (1 paper)Cancers (1 paper)Advanced Quantum Technologies (1 paper)Diagnostics (1 paper)
- Partner nations
- ItalySwitzerlandGermany
In The Last Decade
Asel Sagingalieva
7 papers receiving 180 citations
Asel Sagingalieva's Hit Papers
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 137
- Biophysics 12
- Computational Theory and Mathematics 30
- Media Technology 10
- Statistical and Nonlinear Physics 14
Countries citing papers authored by Asel Sagingalieva
This map shows the geographic impact of Asel Sagingalieva'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 Asel Sagingalieva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Asel Sagingalieva more than expected).
Fields of papers citing papers by Asel Sagingalieva
This network shows the impact of papers produced by Asel Sagingalieva. 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 Asel Sagingalieva. The network helps show where Asel Sagingalieva may publish in the future.
Co-authors
The 5 scholars most cited alongside Asel Sagingalieva, 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 | Quantum machine learning for image classification Hit paper breakdown → | 2024 | 64 |
| 2 | 2023 | 62 | |
| 3 | 2023 | 25 | |
| 4 | 2024 | 23 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 10 | |
| 7 | 2025 | 1 |
About Asel Sagingalieva
Asel Sagingalieva is a scholar working on Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Epidemiology, having authored 7 papers that have together received 198 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (4 papers), Quantum Information and Cryptography (2 papers), Spectroscopy Techniques in Biomedical and Chemical Research (2 papers), Adversarial Robustness in Machine Learning (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Parallel Computing and Optimization Techniques (1 paper), Liver Disease Diagnosis and Treatment (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Artificial Intelligence (137 citations), Biophysics (12 citations), Computational Theory and Mathematics (30 citations), Media Technology (10 citations) and Statistical and Nonlinear Physics (14 citations). Asel Sagingalieva has collaborated with scholars based in Italy, Switzerland and Germany. Frequent co-authors include Alexey Melnikov, W. Somogyi, Fábio Cavalli, Deborah Bonazza and Fabrizio Zanconati. Their work appears in journals such as Machine Learning Science and Technology, IEEE Transactions on Quantum Engineering, Cancers, Advanced Quantum Technologies and Diagnostics.
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.