Simon Mak
Impact in
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- Probabilistic and Robust Engineering Design
- Statistics and Probability top 5%
- Statistical Methods and Inference
Papers in
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- Gaussian Processes and Bayesian Inference 7
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- Combustion and flame dynamics 4
- Computational Fluid Dynamics and Aerodynamics 3
- Co-authors
- V. Roshan Joseph (7 shared papers)Vigor Yang (2 shared papers)C. F. Jeff Wu (2 shared papers)Xingjian Wang (2 shared papers)Changbao Wu (1 shared paper)Jared D. Huling (1 shared paper)Yao Xie (4 shared papers)Jean-François Paquet (2 shared papers)
- Journals
- Technometrics (4 papers)SIAM/ASA Journal on Uncertainty Quantification (3 papers)Journal of the American Statistical Association (3 papers)Statistical Analysis and Data Mining The ASA Data Science Journal (2 papers)Physics Letters A (2 papers)
- Partner nations
- United StatesHong KongChina
In The Last Decade
Simon Mak
29 papers receiving 353 citations
Peers
Comparison fields: 5 of 76
- Statistics, Probability and Uncertainty 85
- Statistics and Probability 61
- Computational Theory and Mathematics 101
- Management Science and Operations Research 65
- Numerical Analysis 21
Countries citing papers authored by Simon Mak
This map shows the geographic impact of Simon Mak'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 Simon Mak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Mak more than expected).
Fields of papers citing papers by Simon Mak
This network shows the impact of papers produced by Simon Mak. 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 Simon Mak. The network helps show where Simon Mak may publish in the future.
Co-authors
The 25 scholars most cited alongside Simon Mak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 85 | |
| 2 | 2018 | 82 | |
| 3 | 2017 | 39 | |
| 4 | 2021 | 20 | |
| 5 | 2022 | 18 | |
| 6 | 2023 | 17 | |
| 7 | 2023 | 12 | |
| 8 | 2024 | 11 | |
| 9 | 2021 | 11 | |
| 10 | 2022 | 9 | |
| 11 | 2024 | 6 | |
| 12 | 2022 | 6 | |
| 13 | 1991 | 6 | |
| 14 | 2022 | 6 | |
| 15 | 2024 | 4 | |
| 16 | 2024 | 3 | |
| 17 | 1960 | 3 | |
| 18 | 2023 | 3 | |
| 19 | 2024 | 2 | |
| 20 | 2023 | 2 |
About Simon Mak
Simon Mak is a scholar working on Artificial Intelligence, Computational Mechanics, Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Management Science and Operations Research, having authored 32 papers that have together received 358 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (7 papers), Probabilistic and Robust Engineering Design (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers), Combustion and flame dynamics (4 papers), Statistical Methods and Inference (3 papers), Advanced Combustion Engine Technologies (3 papers), Computational Fluid Dynamics and Aerodynamics (3 papers) and Mathematical Approximation and Integration (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (85 citations), Statistics and Probability (61 citations), Computational Theory and Mathematics (101 citations), Management Science and Operations Research (65 citations) and Numerical Analysis (21 citations). Simon Mak has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include V. Roshan Joseph, Vigor Yang, C. F. Jeff Wu, Xingjian Wang, Changbao Wu, Jared D. Huling, Yao Xie, Jean-François Paquet, Steffen A. Bass and D. V. Cormack. Their work appears in journals such as Technometrics, SIAM/ASA Journal on Uncertainty Quantification, Journal of the American Statistical Association, Statistical Analysis and Data Mining The ASA Data Science Journal and Physics Letters A.
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.