Y.-T. Lai
Impact in
- Artificial Intelligence top 10%
- Advanced Clustering Algorithms Research
- Bayesian Methods and Mixture Models
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- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
Papers in
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- Particle physics theoretical and experimental studies 13
- Quantum Chromodynamics and Particle Interactions 12
- High-Energy Particle Collisions Research 10
- Black Holes and Theoretical Physics 1
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- Computational Physics and Python Applications 1
- Bayesian Methods and Mixture Models 1
- Co-authors
- W. J. Krzanowski (1 shared paper)
- Journals
- Physical review. D (8 papers)Physical Review Letters (3 papers)Biometrics (1 paper)Journal of High Energy Physics (1 paper)Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields (1 paper)
In The Last Decade
Y.-T. Lai
13 papers receiving 583 citations
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 206
- Nuclear and High Energy Physics 72
- Signal Processing 56
- Statistics and Probability 33
- Computer Vision and Pattern Recognition 65
Countries citing papers authored by Y.-T. Lai
This map shows the geographic impact of Y.-T. Lai'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 Y.-T. Lai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Y.-T. Lai more than expected).
Fields of papers citing papers by Y.-T. Lai
This network shows the impact of papers produced by Y.-T. Lai. 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 Y.-T. Lai. The network helps show where Y.-T. Lai may publish in the future.
Co-authors
The 1 scholars most cited alongside Y.-T. Lai, 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 | 1988 | 544 | |
| 2 | 2017 | 15 | |
| 3 | 2022 | 12 | |
| 4 | 2015 | 9 | |
| 5 | 2022 | 9 | |
| 6 | 2021 | 6 | |
| 7 | 2019 | 5 | |
| 8 | 2014 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2022 | 3 | |
| 11 | 2019 | 3 | |
| 12 | 2022 | 3 | |
| 13 | 2019 | 2 | |
| 14 | 2023 | 0 |
About Y.-T. Lai
Y.-T. Lai is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Insect Science, having authored 14 papers that have together received 619 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (13 papers), Quantum Chromodynamics and Particle Interactions (12 papers), High-Energy Particle Collisions Research (10 papers), Computational Physics and Python Applications (1 paper), Face and Expression Recognition (1 paper), Particle Accelerators and Free-Electron Lasers (1 paper), Black Holes and Theoretical Physics (1 paper) and Bayesian Methods and Mixture Models (1 paper). The work is most often cited by research in Artificial Intelligence (206 citations), Nuclear and High Energy Physics (72 citations), Signal Processing (56 citations), Statistics and Probability (33 citations) and Computer Vision and Pattern Recognition (65 citations). Y.-T. Lai has collaborated with scholars based in Japan, Russia and Slovenia. Frequent co-authors include W. J. Krzanowski. Their work appears in journals such as Physical review. D, Physical Review Letters, Biometrics, Journal of High Energy Physics and Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields.
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.