Chris J. Oates

1.5k total citations
48 papers, 527 citations indexed

About

Chris J. Oates is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Chris J. Oates has authored 48 papers receiving a total of 527 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 17 papers in Statistics and Probability and 10 papers in Molecular Biology. Recurrent topics in Chris J. Oates's work include Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (8 papers) and Statistical Methods and Bayesian Inference (8 papers). Chris J. Oates is often cited by papers focused on Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (8 papers) and Statistical Methods and Bayesian Inference (8 papers). Chris J. Oates collaborates with scholars based in United Kingdom, United States and Australia. Chris J. Oates's co-authors include Mark Girolami, Sach Mukherjee, Nicolás Chopin, Theodore Papamarkou, François‐Xavier Briol, Joe W. Gray, T. J. Sullivan, Nial Friel, James E. Korkola and Nora Bayani and has published in prestigious journals such as Nature Communications, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Chris J. Oates

46 papers receiving 508 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chris J. Oates United Kingdom 14 166 151 140 60 52 48 527
Cun-Hui Zhang United States 11 194 1.2× 449 3.0× 104 0.7× 70 1.2× 16 0.3× 16 825
Bernhard Schmitzer Germany 10 83 0.5× 61 0.4× 52 0.4× 17 0.3× 45 0.9× 32 553
Arnak S. Dalalyan France 11 170 1.0× 321 2.1× 31 0.2× 29 0.5× 23 0.4× 34 484
Marianna Pensky United States 14 152 0.9× 541 3.6× 105 0.8× 261 4.3× 45 0.9× 50 927
Mauro Piccioni Italy 13 180 1.1× 123 0.8× 21 0.1× 19 0.3× 20 0.4× 41 583
Wei‐Liem Loh Singapore 12 115 0.7× 228 1.5× 15 0.1× 61 1.0× 35 0.7× 25 487
Patricia Reynaud-Bouret France 12 84 0.5× 124 0.8× 108 0.8× 20 0.3× 49 0.9× 32 383
Steven B. Damelin United States 13 80 0.5× 87 0.6× 63 0.5× 11 0.2× 17 0.3× 56 545
Osnat Stramer United States 13 164 1.0× 228 1.5× 23 0.2× 44 0.7× 34 0.7× 23 435
Sébastien Gadat France 11 82 0.5× 65 0.4× 48 0.3× 6 0.1× 21 0.4× 34 386

Countries citing papers authored by Chris J. Oates

Since Specialization
Citations

This map shows the geographic impact of Chris J. Oates'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 Chris J. Oates with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris J. Oates more than expected).

Fields of papers citing papers by Chris J. Oates

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris J. Oates. 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 Chris J. Oates. The network helps show where Chris J. Oates may publish in the future.

Co-authorship network of co-authors of Chris J. Oates

This figure shows the co-authorship network connecting the top 25 collaborators of Chris J. Oates. A scholar is included among the top collaborators of Chris J. Oates 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 Chris J. Oates. Chris J. Oates is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Strocchi, Marina, Christoph M. Augustin, Matthias A. F. Gsell, et al.. (2025). Integrating Imaging and Invasive Pressure Data into a Multiscale Whole-Heart Model. Journal of Biomechanical Engineering. 148(5). 1 indexed citations
2.
Porcu, Emilio, Moreno Bevilacqua, Robert Schaback, & Chris J. Oates. (2024). The Matérn Model: A Journey Through Statistics, Numerical Analysis and Machine Learning. Statistical Science. 39(3). 7 indexed citations
3.
Strocchi, Marina, Christoph M. Augustin, Matthias A. F. Gsell, et al.. (2023). Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators. PLoS Computational Biology. 19(6). e1011257–e1011257. 37 indexed citations
4.
Teymur, Onur, et al.. (2022). Post-Processing of MCMC.. PubMed. 9. 529–555. 1 indexed citations
5.
Dodwell, Tim, C Buchanan, Pinelopi Kyvelou, et al.. (2021). A data-centric approach to generative modelling for 3D-printed steel. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 477(2255). 20210444–20210444. 13 indexed citations
6.
Briol, François‐Xavier, Robert E. Gaunt, Jackson Gorham, et al.. (2021). Stein's Method Meets Statistics: A Review of Some Recent Developments. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 3 indexed citations
7.
Teymur, Onur, et al.. (2021). Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. Kent Academic Repository (University of Kent). 1027–1035. 2 indexed citations
8.
Briol, François‐Xavier, Robert E. Gaunt, Jackson Gorham, et al.. (2021). Stein's Method Meets Computational Statistics: A Review of Some Recent Developments. arXiv (Cornell University). 1 indexed citations
9.
Oates, Chris J., et al.. (2019). A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis. 15(4). 2 indexed citations
10.
Oates, Chris J. & T. J. Sullivan. (2019). A modern retrospective on probabilistic numerics. Newcastle University ePrints (Newcastle Univesity). 16 indexed citations
11.
Oates, Chris J., Jessica Kasza, J. A. Simpson, & Andrew Forbes. (2017). Repair of Partly Misspecified Causal Diagrams. Epidemiology. 28(4). 548–552. 8 indexed citations
12.
Oates, Chris J. & Sach Mukherjee. (2016). Causal Discovery as Semi-Supervised Learning. arXiv (Cornell University). 1 indexed citations
13.
Oates, Chris J. & Sach Mukherjee. (2016). NETWORK INFERENCE AND BIOLOGICAL DYNAMICS1. 28 indexed citations
14.
Harjanto, Dewi, et al.. (2016). RNA editing generates cellular subsets with diverse sequence within populations. Nature Communications. 7(1). 12145–12145. 34 indexed citations
15.
Oates, Chris J. & Mark Girolami. (2016). Control Functionals for Quasi-Monte Carlo Integration. Cambridge University Engineering Department Publications Database. 56–65. 3 indexed citations
16.
Oates, Chris J., Mark Girolami, & Nicolás Chopin. (2016). Control Functionals for Monte Carlo Integration. Journal of the Royal Statistical Society Series B (Statistical Methodology). 79(3). 695–718. 58 indexed citations
17.
Briol, François‐Xavier, Chris J. Oates, Mark Girolami, Michael A. Osborne, & Dino Sejdinović. (2015). Probabilistic Integration. arXiv (Cornell University). 2 indexed citations
18.
Briol, François‐Xavier, Chris J. Oates, Mark Girolami, & Michael A. Osborne. (2015). Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. arXiv (Cornell University). 28. 1162–1170. 12 indexed citations
19.
Saha, Krishanu, et al.. (2014). A stochastic model dissects cell states in biological transition processes. Scientific Reports. 4(1). 3692–3692. 20 indexed citations
20.
Casale, Francesco Paolo, Giorgio Giurato, Giovanni Nassa, et al.. (2014). Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS ONE. 9(2). e88485–e88485. 3 indexed citations

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.

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