Mark Girolami

19.6k total citations · 6 hit papers
236 papers, 10.4k citations indexed

About

Mark Girolami is a scholar working on Artificial Intelligence, Molecular Biology and Signal Processing. According to data from OpenAlex, Mark Girolami has authored 236 papers receiving a total of 10.4k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Artificial Intelligence, 44 papers in Molecular Biology and 36 papers in Signal Processing. Recurrent topics in Mark Girolami's work include Gaussian Processes and Bayesian Inference (39 papers), Blind Source Separation Techniques (29 papers) and Probabilistic and Robust Engineering Design (25 papers). Mark Girolami is often cited by papers focused on Gaussian Processes and Bayesian Inference (39 papers), Blind Source Separation Techniques (29 papers) and Probabilistic and Robust Engineering Design (25 papers). Mark Girolami collaborates with scholars based in United Kingdom, United States and Germany. Mark Girolami's co-authors include Terrence J. Sejnowski, Ben Calderhead, Te-Won Lee, Simon Rogers, Ata Kabán, Theodoros Damoulas, Ioannis Brilakis, Rafael Sacks, Vladislav Vyshemirsky and Chao He and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and SHILAP Revista de lepidopterología.

In The Last Decade

Mark Girolami

230 papers receiving 9.8k citations

Hit Papers

Independent Component Analysis Using an Extended Infomax ... 1999 2026 2008 2017 1999 2011 2002 2021 2020 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Girolami United Kingdom 51 3.3k 1.9k 1.4k 1.4k 1.3k 236 10.4k
David Mackay United Kingdom 48 6.0k 1.8× 1.1k 0.5× 1.6k 1.1× 1.2k 0.8× 1.5k 1.2× 159 20.4k
Radford M. Neal Canada 31 8.3k 2.5× 1.9k 1.0× 1.5k 1.0× 1.3k 0.9× 3.5k 2.8× 55 19.8k
Éric Moulines France 45 5.1k 1.5× 5.8k 3.0× 432 0.3× 895 0.6× 991 0.8× 252 12.7k
Colin Goodall United States 16 2.4k 0.7× 1.1k 0.6× 931 0.6× 442 0.3× 2.2k 1.7× 34 10.4k
Chris Bishop United Kingdom 35 9.2k 2.8× 3.0k 1.5× 1.7k 1.2× 1.2k 0.8× 5.4k 4.3× 100 24.0k
Krzysztof J. Cios United States 29 6.3k 1.9× 1.2k 0.6× 1.6k 1.1× 1.1k 0.8× 3.7k 2.9× 128 14.8k
Kevin P. Murphy Canada 25 3.9k 1.2× 982 0.5× 1.0k 0.7× 439 0.3× 1.6k 1.3× 38 10.1k
Masashi Sugiyama Japan 47 5.4k 1.6× 1.3k 0.7× 385 0.3× 538 0.4× 3.1k 2.4× 419 10.1k
Michel Verleysen Belgium 44 4.1k 1.2× 1.0k 0.5× 594 0.4× 598 0.4× 2.5k 1.9× 287 8.7k
Lars Kai Hansen Denmark 46 3.7k 1.1× 1.8k 0.9× 1.0k 0.7× 2.9k 2.1× 1.7k 1.3× 332 11.8k

Countries citing papers authored by Mark Girolami

Since Specialization
Citations

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

Fields of papers citing papers by Mark Girolami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Girolami. 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 Mark Girolami. The network helps show where Mark Girolami may publish in the future.

Co-authorship network of co-authors of Mark Girolami

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Girolami. A scholar is included among the top collaborators of Mark Girolami 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 Mark Girolami. Mark Girolami 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.
Kuok, Sin‐Chi, S. Yao, Ka‐Veng Yuen, Wang‐Ji Yan, & Mark Girolami. (2025). Bayesian generative kernel Gaussian process regression. Mechanical Systems and Signal Processing. 227. 112395–112395. 4 indexed citations
2.
Hadjidemetriou, Georgios M., et al.. (2024). On the performance of pothole detection algorithms enhanced via data augmentation. Transportation research procedia. 78. 230–237. 3 indexed citations
3.
Girolami, Mark, et al.. (2024). Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation. Journal of Computational Physics. 515. 113293–113293. 2 indexed citations
4.
Pavliotis, Grigorios A., et al.. (2023). Neural parameter calibration for large-scale multiagent models. Proceedings of the National Academy of Sciences. 120(7). e2216415120–e2216415120. 13 indexed citations
5.
Cirak, Fehmi, et al.. (2022). Variational Bayesian approximation of inverse problems using sparse precision matrices. Computer Methods in Applied Mechanics and Engineering. 393. 114712–114712. 14 indexed citations
6.
Duncan, A., et al.. (2022). Bayesian assessments of aeroengine performance with transfer learning. SHILAP Revista de lepidopterología. 3. 3 indexed citations
7.
Sacks, Rafael, Ioannis Brilakis, Ergo Pikas, Haiyan Xie, & Mark Girolami. (2020). Construction with digital twin information systems. SHILAP Revista de lepidopterología. 1. 326 indexed citations breakdown →
8.
Girolami, Mark, et al.. (2019). The Statistical Finite Element Method. arXiv (Cornell University). 1 indexed citations
9.
Virtanen, Seppo & Mark Girolami. (2019). Precision-Recall Balanced Topic Modelling. Cambridge University Engineering Department Publications Database. 32. 6747–6756. 4 indexed citations
10.
Girolami, Mark, et al.. (2019). A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability. IEEE Access. 7. 183996–184007. 10 indexed citations
11.
Oates, Chris J. & Mark Girolami. (2016). Control Functionals for Quasi-Monte Carlo Integration. Cambridge University Engineering Department Publications Database. 56–65. 3 indexed citations
12.
Osborne, Michael A., et al.. (2015). Probabilistic numerics and uncertainty in computations. 93 indexed citations
13.
Stathopoulos, Vassilios, Veronica Zamora‐Gutierrez, Kate E. Jones, & Mark Girolami. (2014). Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel. ePrints Soton (University of Southampton). 913–921. 8 indexed citations
14.
Marquand, André F., Maurizio Filippone, John Ashburner, et al.. (2013). Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach. PLoS ONE. 8(7). e69237–e69237. 34 indexed citations
15.
Girolami, Mark, Anne-Marie Lyne, Heiko Strathmann, Daniel Simpson, & Yves F. Atchadé. (2013). Playing Russian Roulette with Intractable Likelihoods. arXiv (Cornell University). 9 indexed citations
16.
Ying, Yiming, Colin Campbell, & Mark Girolami. (2009). Analysis of SVM with Indefinite Kernels. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 22. 2205–2213. 35 indexed citations
17.
Girolami, Mark & Ata Kabán. (2003). Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. UCL Discovery (University College London). 16. 9–16. 24 indexed citations
18.
Girolami, Mark. (2000). A generative model for sparse discrete binary data with non-uniform categorical priors.. The European Symposium on Artificial Neural Networks. 1–6. 3 indexed citations
19.
Girolami, Mark & Colin Fyfe. (1997). Independence is far from normal.. The European Symposium on Artificial Neural Networks. 2 indexed citations
20.
Girolami, Mark. (1996). Negentropy and Kurtosis as Projection Pursuit Indices Provide Generalised ICA Algorithms. Neural Information Processing Systems. 34 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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