Alexis Boukouvalas

1.3k total citations
23 papers, 594 citations indexed

About

Alexis Boukouvalas is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Alexis Boukouvalas has authored 23 papers receiving a total of 594 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Molecular Biology and 5 papers in Computational Theory and Mathematics. Recurrent topics in Alexis Boukouvalas's work include Single-cell and spatial transcriptomics (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers) and Gaussian Processes and Bayesian Inference (5 papers). Alexis Boukouvalas is often cited by papers focused on Single-cell and spatial transcriptomics (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers) and Gaussian Processes and Bayesian Inference (5 papers). Alexis Boukouvalas collaborates with scholars based in United Kingdom, United States and Australia. Alexis Boukouvalas's co-authors include Yordan P. Raykov, Max A. Little, Fahd Baig, Magnus Rattray, Ganesh Dasika, Emre Özer, Dan Cornford, Luisa Cutillo, James Hensman and Claudia Angelini and has published in prestigious journals such as Bioinformatics, PLoS ONE and Technometrics.

In The Last Decade

Alexis Boukouvalas

23 papers receiving 577 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexis Boukouvalas United Kingdom 12 161 93 58 49 49 23 594
Stuart Barber United Kingdom 12 93 0.6× 67 0.7× 22 0.4× 52 1.1× 8 0.2× 32 621
Zhaoyi Chen United States 18 187 1.2× 224 2.4× 24 0.4× 91 1.9× 27 0.6× 64 1.3k
Mohammed Khalilia United States 7 81 0.5× 346 3.7× 31 0.5× 52 1.1× 13 0.3× 22 743
Mu Zhu Canada 18 259 1.6× 276 3.0× 19 0.3× 159 3.2× 30 0.6× 42 1.1k
Haohui Lu Australia 12 67 0.4× 272 2.9× 26 0.4× 42 0.9× 7 0.1× 27 836
Damjan Krstajić United States 5 132 0.8× 110 1.2× 27 0.5× 25 0.5× 4 0.1× 8 786
Sounak Chakraborty United States 13 180 1.1× 322 3.5× 40 0.7× 61 1.2× 7 0.1× 33 897
Youngho Lee South Korea 16 55 0.3× 199 2.1× 15 0.3× 59 1.2× 20 0.4× 57 759
Trang T. Le United States 14 130 0.8× 158 1.7× 28 0.5× 28 0.6× 3 0.1× 36 849
Zhihua Tang China 15 37 0.2× 42 0.5× 100 1.7× 13 0.3× 13 0.3× 52 706

Countries citing papers authored by Alexis Boukouvalas

Since Specialization
Citations

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

Fields of papers citing papers by Alexis Boukouvalas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexis Boukouvalas

This figure shows the co-authorship network connecting the top 25 collaborators of Alexis Boukouvalas. A scholar is included among the top collaborators of Alexis Boukouvalas 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 Alexis Boukouvalas. Alexis Boukouvalas 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.
John, St., et al.. (2021). Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinformatics. 37(21). 3788–3795. 28 indexed citations
2.
Holland, Carol, Alexis Boukouvalas, Danielle Clarkesmith, & Richard Cooke. (2021). Specific Autobiographical Recall Mediates Impact of Cognition and Depression on Independence Function and Well-Being in Older Adults. Frontiers in Psychology. 12. 652600–652600. 2 indexed citations
3.
Wade, Sara, et al.. (2020). Enriched mixtures of generalised Gaussian process experts.. Aaltodoc (Aalto University). 3144–3154. 2 indexed citations
4.
Cutillo, Luisa, Alexis Boukouvalas, Elli Marinopoulou, Nancy Papalopulu, & Magnus Rattray. (2020). OscoNet: inferring oscillatory gene networks. BMC Bioinformatics. 21(S10). 351–351. 2 indexed citations
5.
Raykov, Yordan P., et al.. (2019). Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods. Frontiers in Genetics. 10. 1253–1253. 42 indexed citations
6.
Boukouvalas, Alexis, et al.. (2019). Decision Variance in Risk-Averse Online Learning. Research Explorer (The University of Manchester). 7. 2738–2744. 3 indexed citations
7.
Rattray, Magnus, et al.. (2018). GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics. 35(1). 47–54. 31 indexed citations
8.
Boukouvalas, Alexis, James Hensman, & Magnus Rattray. (2018). BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process. Genome biology. 19(1). 65–65. 16 indexed citations
9.
Holland, Carol, et al.. (2016). Transition from community dwelling to retirement village in older adults: cognitive functioning and psychological health outcomes. Ageing and Society. 37(7). 1499–1526. 36 indexed citations
10.
Raykov, Yordan P., Alexis Boukouvalas, Fahd Baig, & Max A. Little. (2016). What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm. PLoS ONE. 11(9). e0162259–e0162259. 135 indexed citations
11.
Raykov, Yordan P., Alexis Boukouvalas, & Max A. Little. (2016). Simple approximate MAP inference for Dirichlet processes mixtures. Electronic Journal of Statistics. 10(2). 14 indexed citations
12.
Boukouvalas, Alexis, Wai‐Yee Chan, Ian Maidment, et al.. (2015). Does anticholinergics drug burden relate to global neuro-disability outcome measures and length of hospital stay?. Brain Injury. 29(12). 1426–1430. 7 indexed citations
13.
Pararasa, Chathyan, Alexis Boukouvalas, Ian T. Nabney, et al.. (2015). Age‐associated changes in long‐chain fatty acid profile during healthy aging promote pro‐inflammatory monocyte polarization via PPAR γ. Aging Cell. 15(1). 128–139. 64 indexed citations
14.
Boukouvalas, Alexis, et al.. (2014). Bayesian Precalibration of a Large Stochastic Microsimulation Model. IEEE Transactions on Intelligent Transportation Systems. 15(3). 1337–1347. 9 indexed citations
15.
Boukouvalas, Alexis, Dan Cornford, & Alexander Singer. (2013). Managing Uncertainty in Complex Stochastic Models: Design and Emulation of a Rabies Model1 3. Aston Publications Explorer (Aston University). 3 indexed citations
16.
Boukouvalas, Alexis, Dan Cornford, & Milan Stehlík. (2013). Optimal design for correlated processes with input-dependent noise. Computational Statistics & Data Analysis. 71. 1088–1102. 20 indexed citations
17.
Boukouvalas, Alexis, et al.. (2013). An Efficient Screening Method for Computer Experiments. Technometrics. 56(4). 422–431. 18 indexed citations
18.
Schneider, Carl R., et al.. (2013). Medication reconciliation by a pharmacy technician in a mental health assessment unit. International Journal of Clinical Pharmacy. 36(2). 303–309. 50 indexed citations
19.
Boukouvalas, Alexis, et al.. (2012). Gaussian Process Quantile Regression using Expectation Propagation. arXiv (Cornell University). 939–946. 7 indexed citations
20.
Boukouvalas, Alexis & Dan Cornford. (2009). Learning Heteroscedastic Gaussian Processes for Complex Datasets. 11 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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