Faming Liang
- Statistics and Probability top 0.5%
- Markov Chains and Monte Carlo Methods 39
- Statistical Methods and Inference 30
- Statistical Methods and Bayesian Inference 13
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 36
- Gaussian Processes and Bayesian Inference 13
- Neural Networks and Applications 11
- Environmental Engineering top 5%
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- Gene expression and cancer classification 26
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- Theoretical and Computational Physics 12
- Co-authors
- Wing Hung WongJianhua HuangChuanhai LiuRaymond J. CarrollJun S. LiuQifan SongYuanchang XieYunlong Zhang
- Journals
- Journal of the American Statistical Association (18 papers)Journal of Computational and Graphical Statistics (7 papers)Statistics and Computing (7 papers)
- Partner nations
- United StatesSingaporeSouth Korea
In The Last Decade
Faming Liang
140 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 177
- Statistics and Probability 911
- Artificial Intelligence 1.2k
- Statistics, Probability and Uncertainty 181
- Safety, Risk, Reliability and Quality 188
- Environmental Engineering 251
Countries citing papers authored by Faming Liang
This map shows the geographic impact of Faming Liang'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 Faming Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Faming Liang more than expected).
Fields of papers citing papers by Faming Liang
This network shows the impact of papers produced by Faming Liang. 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 Faming Liang. The network helps show where Faming Liang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Faming Liang, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 0 | |
| 4 | 2022 | 0 | |
| 5 | 2019 | 9 | |
| 6 | 2019 | 4 | |
| 7 | 2019 | 12 | |
| 8 | 2018 | 12 | |
| 9 | 2016 | 132 | |
| 10 | 2014 | 9 | |
| 11 | 2014 | 1 | |
| 12 | 2013 | 46 | |
| 13 | 2011 | 6 | |
| 14 | 2010 | 19 | |
| 15 | 2009 | 93 | |
| 16 | 2008 | 4 | |
| 17 | A ROBUST SEQUENTIAL BAYESIAN METHOD FOR IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES | 2007 | 12 |
| 18 | Crash Injury Severity Analysis Using a Bayesian Ordered Probit Model | 2007 | 8 |
| 19 | 2004 | 3 | |
| 20 | 2003 | 9 |
About Faming Liang
Faming Liang is a scholar working on Statistics and Probability, Artificial Intelligence, Condensed Matter Physics, Mathematical Physics and Numerical Analysis, having authored 148 papers that have together received 3.4k indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (39 papers), Bayesian Methods and Mixture Models (36 papers), Statistical Methods and Inference (30 papers), Gene expression and cancer classification (26 papers), Statistical Methods and Bayesian Inference (13 papers), Gaussian Processes and Bayesian Inference (13 papers), Theoretical and Computational Physics (12 papers) and Neural Networks and Applications (11 papers). The work is most often cited by research in Statistics and Probability (911 citations), Artificial Intelligence (1.2k citations), Statistics, Probability and Uncertainty (181 citations), Safety, Risk, Reliability and Quality (188 citations) and Environmental Engineering (251 citations). Faming Liang has collaborated with scholars based in United States, Singapore and South Korea. Frequent co-authors include Wing Hung Wong, Jianhua Huang, Chuanhai Liu, Raymond J. Carroll, Jun S. Liu, Qifan Song, Yuanchang Xie, Yunlong Zhang, Guanghua Xiao and Xuesong Zhang. Their work appears in journals such as Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Statistics and Computing, Biostatistics and Computational Statistics & Data Analysis.
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.