Matthew Praeger

866 total citations
61 papers, 646 citations indexed

About

Matthew Praeger is a scholar working on Biomedical Engineering, Materials Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Matthew Praeger has authored 61 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Biomedical Engineering, 16 papers in Materials Chemistry and 15 papers in Electrical and Electronic Engineering. Recurrent topics in Matthew Praeger's work include High voltage insulation and dielectric phenomena (14 papers), Dielectric materials and actuators (14 papers) and Laser-Matter Interactions and Applications (10 papers). Matthew Praeger is often cited by papers focused on High voltage insulation and dielectric phenomena (14 papers), Dielectric materials and actuators (14 papers) and Laser-Matter Interactions and Applications (10 papers). Matthew Praeger collaborates with scholars based in United Kingdom, Germany and Brazil. Matthew Praeger's co-authors include A. S. Vaughan, S.G. Swingler, Ben Mills, I. L. Hosier, James A. Grant‐Jacob, R.W. Eason, Yunhui Xie, A. F. Holt, Vladan Vuletić and C. Zimmermann and has published in prestigious journals such as Physical Review Letters, Applied Physics Letters and Analytical Biochemistry.

In The Last Decade

Matthew Praeger

59 papers receiving 611 citations

Peers

Countries citing papers authored by Matthew Praeger

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Praeger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Praeger

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment 2025 ·Scientific Reports ·Joseph C. Scott, James A. Grant‐Jacob, Matthew Praeger, (unknown), Jonathan Sutton, Michalis N. Zervas, Mahesan Niranjan, R.W. Eason, Eugene Healy, Ben Mills	1
2	Single-step phase identification and phase locking for coherent beam combination using deep learning 2024 ·Scientific Reports ·Yunhui Xie, (unknown), Ben Mills, Yuchen Liu, Matthew Praeger, James A. Grant‐Jacob, Michalis N. Zervas	4
3	Single-step beam intensity and profile optimization using a 256 × 256 micromirror array and reinforcement learning 2024 ·Optics Express ·Yunhui Xie, Matthew Praeger, James A. Grant‐Jacob, Ben Mills	1
4	Characterisation of engineered defects in extreme ultraviolet mirror substrates using lab-scale extreme ultraviolet reflection ptychography 2023 ·Ultramicroscopy ·(unknown), Michal Odstrčil, (unknown), (unknown), (unknown), Guanze He, Matthew Praeger, Larissa Juschkin, Jeremy G. Frey, W.S. Brocklesby	8
5	Single step phase optimisation for coherent beam combination using deep learning 2022 ·Scientific Reports ·Ben Mills, James A. Grant‐Jacob, Matthew Praeger, R.W. Eason, Johan Nilsson, Michalis N. Zervas	25
6	Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation 2021 ·Environmental Research Communications ·James A. Grant‐Jacob, Matthew Praeger, Matthew Loxham, R.W. Eason, Ben Mills	1
7	Modelling of fibre laser cutting via deep learning 2021 ·Optics Express ·(unknown), (unknown), Matthew Praeger, James A. Grant‐Jacob, Christophe A. Codemard, Paul Harrison, Ben Mills, Michalis N. Zervas	13
8	Predictive Visualisation of Fibre Laser Machining via Deep Learning 2021 ·ePrints Soton (University of Southampton) ·(unknown), (unknown), Matthew Praeger, James A. Grant‐Jacob, Christophe A. Codemard, Paul Harrison, Ben Mills, Michalis N. Zervas	1
9	Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining 2021 ·Journal of Intelligent Manufacturing ·(unknown), (unknown), (unknown), James A. Grant‐Jacob, Matthew Praeger, Dimitris Karnakis, R.W. Eason, Ben Mills	45
10	Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning 2020 ·(unknown), (unknown), (unknown), Yunhui Xie, (unknown), Matthew Praeger, James A. Grant‐Jacob, R.W. Eason, Rohan M. Lewis, Ben Mills	1
11	Lensless imaging of pollen grains at three-wavelengths using deep learning 2020 ·Environmental Research Communications ·James A. Grant‐Jacob, Matthew Praeger, Matthew Loxham, R.W. Eason, Ben Mills	17
12	Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning 2020 ·ePrints Soton (University of Southampton) ·(unknown), (unknown), (unknown), Yunhui Xie, (unknown), Matthew Praeger, James A. Grant‐Jacob, R.W. Eason, Rohan M. Lewis, Ben Mills	3
13	Fibre-optic based particle sensing via deep learning 2019 ·Journal of Physics Photonics ·James A. Grant‐Jacob, Saurabh Jain, Yunhui Xie, (unknown), (unknown), Matthew Praeger, Matthew Loxham, David J. Richardson, R.W. Eason, Ben Mills	19
14	Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi 2019 ·Environmental Research Communications ·James A. Grant‐Jacob, Yunhui Xie, (unknown), Matthew Praeger, (unknown), Daniel J. Heath, Matthew Loxham, R.W. Eason, Ben Mills	25
15	Calcined silica for enhanced polyethylene nano composites 2015 ·ePrints Soton (University of Southampton) ·Matthew Praeger, I. L. Hosier, A. S. Vaughan, S.G. Swingler	2
16	A simple theoretical model for the bulk properties of nanocomposite materials 2014 ·ePrints Soton (University of Southampton) ·Matthew Praeger, Thomas Andritsch, S.G. Swingler, A. S. Vaughan	8
17	The direct writing and focusing of nanoparticles generated by an electrical discharge 2012 ·Journal of Nanoparticle Research ·Ehab Saleh, Matthew Praeger, A. S. Vaughan, Will Stewart, W.H. Loh	2
18	Uniform aligned bioconjugation of biomolecule motifs for integration within microfabricated microfluidic devices 2012 ·Analytical Biochemistry ·(unknown), Patrick Garland, Matthew Praeger, (unknown), Daniel Friedrich, David Morgan, Tracy Melvin	1
19	Fabrication of nanoscale glass fibers by electrospinning 2012 ·Applied Physics Letters ·Matthew Praeger, Ehab Saleh, A. S. Vaughan, Will Stewart, W.H. Loh	14
20	Soft-x-ray wavelength shift induced by ionization effects in a capillary 2006 ·Optics Letters ·(unknown), (unknown), D.C. Hanna, W.S. Brocklesby, Matthew Praeger, Ana Paula, Jeremy J. Baumberg, Jeremy G. Frey	43

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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