Daniel Sanz-Alonso
- Artificial Intelligence top 10%
- Statistics and Probability top 5%
- Atmospheric Science
- Statistical and Nonlinear Physics top 10%
- Statistics, Probability and Uncertainty top 10%
- Co-authors
- Andrew M. StuartSergios AgapiouOmiros PapaspiliopoulosNicolás García TrillosRebecca WillettKody J. H. LawJohn HarlimM. P. Calvo
- Topics
- Gaussian Processes and Bayesian Inference (14 papers)Statistical Methods and Inference (6 papers)Probabilistic and Robust Engineering Design (5 papers)
- Journals
- Journal of Computational PhysicsSIAM Journal on Numerical AnalysisPhysica D Nonlinear Phenomena
- Partner nations
- United StatesUnited KingdomSpain
In The Last Decade
Daniel Sanz-Alonso
24 papers receiving 209 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 111
- Statistics and Probability 72
- Atmospheric Science 44
- Statistical and Nonlinear Physics 39
- Statistics, Probability and Uncertainty 31
Countries citing papers authored by Daniel Sanz-Alonso
This map shows the geographic impact of Daniel Sanz-Alonso'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 Daniel Sanz-Alonso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Sanz-Alonso more than expected).
Fields of papers citing papers by Daniel Sanz-Alonso
This network shows the impact of papers produced by Daniel Sanz-Alonso. 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 Daniel Sanz-Alonso. The network helps show where Daniel Sanz-Alonso may publish in the future.
Co-authorship network of co-authors of Daniel Sanz-Alonso
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Sanz-Alonso. A scholar is included among the top collaborators of Daniel Sanz-Alonso based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Sanz-Alonso. Daniel Sanz-Alonso is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 8 | |
| 11 | 5 | |
| 12 | 1 | |
| 13 | 7 | |
| 14 | 10 | |
| 15 | 7 | |
| 16 | 3 | |
| 17 | On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms | 6 |
| 18 | Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis | 1 |
| 19 | 74 | |
| 20 | 16 |
About Daniel Sanz-Alonso
Daniel Sanz-Alonso is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 27 papers that have together received 227 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (14 papers), Statistical Methods and Inference (6 papers) and Probabilistic and Robust Engineering Design (5 papers). The work is most often cited by research in Statistics and Probability (72 citations), Statistics, Probability and Uncertainty (31 citations) and Artificial Intelligence (111 citations). Daniel Sanz-Alonso has collaborated with scholars based in United States, United Kingdom and Spain. Frequent co-authors include Andrew M. Stuart, Sergios Agapiou, Omiros Papaspiliopoulos, Nicolás García Trillos, Rebecca Willett, Kody J. H. Law, John Harlim, M. P. Calvo, Zijian Wang and J. M. Sanz‐Serna. Their work appears in journals such as Journal of Computational Physics, SIAM Journal on Numerical Analysis and Physica D Nonlinear Phenomena.
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.