Xiaolu Tan
- Finance top 2%
- Stochastic processes and financial applications 32
- Financial Risk and Volatility Modeling 7
- Modeling and Simulation top 5%
- Mathematical Biology Tumor Growth 3
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods 7
- Mathematical Physics top 10%
- Stochastic processes and statistical mechanics 4
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- Risk and Portfolio Optimization 11
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- Economic theories and models 8
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- Insurance, Mortality, Demography, Risk Management 6
Xiaolu Tan
34 papers receiving 368 citations
Peers
Comparison fields: 5 of 41
- Finance 310
- Modeling and Simulation 43
- Statistics and Probability 66
- Mathematical Physics 68
- Management Science and Operations Research 80
Countries citing papers authored by Xiaolu Tan
This map shows the geographic impact of Xiaolu Tan'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 Xiaolu Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaolu Tan more than expected).
Fields of papers citing papers by Xiaolu Tan
This network shows the impact of papers produced by Xiaolu Tan. 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 Xiaolu Tan. The network helps show where Xiaolu Tan may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Xiaolu Tan, 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 | 2 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 8 | |
| 5 | 2022 | 23 | |
| 6 | 2022 | 4 | |
| 7 | 2021 | 7 | |
| 8 | 2018 | 1 | |
| 9 | 2018 | 0 | |
| 10 | 2017 | 5 | |
| 11 | 2017 | 5 | |
| 12 | 2017 | 16 | |
| 13 | 2016 | 11 | |
| 14 | 2016 | 14 | |
| 15 | 2016 | 11 | |
| 16 | 2016 | 8 | |
| 17 | 2016 | 24 | |
| 18 | Stochastic control for a class of nonlinear kernels and applications | 2015 | 32 |
| 19 | 2015 | 5 | |
| 20 | 2013 | 13 |
About Xiaolu Tan
Xiaolu Tan is a scholar working on Finance, Management Science and Operations Research, Statistics and Probability, Mathematical Physics and Modeling and Simulation, having authored 35 papers that have together received 388 indexed citations. Recurring topics across this work include Stochastic processes and financial applications (32 papers), Risk and Portfolio Optimization (11 papers), Economic theories and models (8 papers), Financial Risk and Volatility Modeling (7 papers), Markov Chains and Monte Carlo Methods (7 papers), Insurance, Mortality, Demography, Risk Management (6 papers), Stochastic processes and statistical mechanics (4 papers) and Mathematical Biology Tumor Growth (3 papers). The work is most often cited by research in Finance (310 citations), Modeling and Simulation (43 citations), Statistics and Probability (66 citations), Mathematical Physics (68 citations) and Management Science and Operations Research (80 citations). Xiaolu Tan has collaborated with scholars based in France, Hong Kong and Austria. Frequent co-authors include Nizar Touzi, Dylan Possamaï, Bruno Bouchard, Chao Zhou, Pierre Henry‐Labordère, Zhenjie Ren, Denis Talay, J. Frédéric Bonnans, Xavier Warin and Grégoire Loeper. Their work appears in journals such as SIAM Journal on Control and Optimization, Stochastic Processes and their Applications, The Annals of Applied Probability, The Annals of Probability and Electronic Journal of Probability.
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.