Matthew Lai
Impact in
- Artificial Intelligence top 0.2%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Health Informatics top 1%
Papers in
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- COVID-19 epidemiological studies 1
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- Reinforcement Learning in Robotics 3
- Artificial Intelligence in Games 2
- Neural Networks and Applications 1
- Co-authors
- David SilverThomas HubertArthur GuezKaren SimonyanIoannis AntonoglouJulian SchrittwieserDemis HassabisTimothy Lillicrap
- Journals
- Computer Methods and Programs in Biomedicine (1 paper)Nature (1 paper)Science (1 paper)Journal of Immigrant and Minority Health (1 paper)Science Robotics (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Matthew Lai
7 papers receiving 6.8k citations
Hit Papers
Peers
Comparison fields: 5 of 189
- Artificial Intelligence 3.7k
- Health Informatics 102
- Computer Vision and Pattern Recognition 978
- Control and Systems Engineering 846
- Computational Theory and Mathematics 553
Countries citing papers authored by Matthew Lai
This map shows the geographic impact of Matthew 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 Matthew Lai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Lai more than expected).
Fields of papers citing papers by Matthew Lai
This network shows the impact of papers produced by Matthew 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 Matthew Lai. The network helps show where Matthew Lai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matthew 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 | 2025 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2021 | 11 | |
| 4 | 2021 | 30 | |
| 5 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Hit paper breakdown → | 2018 | 1827 |
| 6 | Mastering the game of Go without human knowledge Hit paper breakdown → | 2017 | 5038 |
| 7 | 2016 | 212 | |
| 8 | A shigellosis outbreak at a primary school in Kuanhsi, Hsinchu County | 1998 | 1 |
About Matthew Lai
Matthew Lai is a scholar working on Modeling and Simulation, Artificial Intelligence, Emergency Medical Services, Health and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 7.1k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Artificial Intelligence in Games (2 papers), Neonatal and fetal brain pathology (1 paper), Influenza Virus Research Studies (1 paper), COVID-19 epidemiological studies (1 paper), Pediatric health and respiratory diseases (1 paper), Neural Networks and Applications (1 paper) and Educational Games and Gamification (1 paper). The work is most often cited by research in Artificial Intelligence (3.7k citations), Health Informatics (102 citations), Computer Vision and Pattern Recognition (978 citations), Control and Systems Engineering (846 citations) and Computational Theory and Mathematics (553 citations). Matthew Lai has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include David Silver, Thomas Hubert, Arthur Guez, Karen Simonyan, Ioannis Antonoglou, Julian Schrittwieser, Demis Hassabis, Timothy Lillicrap, Laurent Sifre and Thore Graepel. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Nature, Science, Journal of Immigrant and Minority Health and Science Robotics.
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.