Paul Rees

6.1k total citations
210 papers, 3.3k citations indexed

About

Paul Rees is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Molecular Biology. According to data from OpenAlex, Paul Rees has authored 210 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Electrical and Electronic Engineering, 79 papers in Atomic and Molecular Physics, and Optics and 41 papers in Molecular Biology. Recurrent topics in Paul Rees's work include Semiconductor Lasers and Optical Devices (57 papers), Semiconductor Quantum Structures and Devices (46 papers) and Photonic and Optical Devices (27 papers). Paul Rees is often cited by papers focused on Semiconductor Lasers and Optical Devices (57 papers), Semiconductor Quantum Structures and Devices (46 papers) and Photonic and Optical Devices (27 papers). Paul Rees collaborates with scholars based in United Kingdom, United States and Ireland. Paul Rees's co-authors include Huw D. Summers, Andrew Filby, Anne E. Carpenter, M. Rowan Brown, John W. Wills, Holger Hennig, Thomas Blasi, P.S. Spencer, Minh Doan and Fabian J. Theis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Physical review. B, Condensed matter.

In The Last Decade

Paul Rees

196 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul Rees United Kingdom	31	865	785	638	613	589	210	3.3k
Nobuhiro Ohta Japan	35	778 0.9×	408 0.5×	1.4k 2.2×	391 0.6×	1.7k 2.9×	341	5.3k
Jay Nadeau United States	33	956 1.1×	958 1.2×	645 1.0×	186 0.3×	1.8k 3.0×	132	3.4k
Huw D. Summers United Kingdom	26	606 0.7×	648 0.8×	836 1.3×	367 0.6×	428 0.7×	148	2.5k
Alexey Popov Russia	34	409 0.5×	1.4k 1.8×	448 0.7×	339 0.6×	571 1.0×	219	3.8k
Haifeng Wang China	27	1.4k 1.6×	1.2k 1.5×	280 0.4×	1.3k 2.1×	703 1.2×	83	4.2k
Liang Hong China	33	990 1.1×	856 1.1×	488 0.8×	153 0.2×	1.3k 2.3×	176	3.8k
Christian D. Lorenz United Kingdom	38	1.4k 1.6×	969 1.2×	502 0.8×	67 0.1×	1.4k 2.3×	202	5.8k
Sang-Hyuk Lee United States	26	482 0.6×	705 0.9×	500 0.8×	534 0.9×	304 0.5×	70	2.8k
Peter Gardner United Kingdom	47	1.2k 1.4×	1.0k 1.3×	717 1.1×	3.0k 4.9×	1.3k 2.3×	193	6.5k
Sadik C. Esener United States	38	1.4k 1.6×	2.6k 3.4×	2.1k 3.3×	237 0.4×	790 1.3×	292	6.1k

Countries citing papers authored by Paul Rees

Since Specialization

Citations

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

Fields of papers citing papers by Paul Rees

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Rees

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Rees. A scholar is included among the top collaborators of Paul Rees 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 Paul Rees. Paul Rees 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

Smith, E. J., et al.. (2025). Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring. Mutagenesis. 41(1-2). 37–45.

McDonald, David, Gillian Hulme, Andrew Fuller, et al.. (2023). OPTIMAL : An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration. Cytometry Part A. 105(1). 36–53. 4 indexed citations

Curran, Scott, G.K. Dey, Paul Rees, & Paul Nurse. (2022). A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle. Proceedings of the National Academy of Sciences. 119(36). e2206172119–e2206172119. 11 indexed citations

Fowden, Abigail L., et al.. (2021). Developing ovine mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment. Communications Biology. 4(1). 993–993. 12 indexed citations

Howard, David, David H. James, Kate Murphy, et al.. (2021). Dinaciclib, a Bimodal Agent Effective against Endometrial Cancer. Cancers. 13(5). 1135–1135. 10 indexed citations

Summers, Huw D., Carla P. Gomes, Aida Varela-Moreira, et al.. (2021). Data-Driven Modeling of the Cellular Pharmacokinetics of Degradable Chitosan-Based Nanoparticles. Nanomaterials. 11(10). 2606–2606. 2 indexed citations

Doan, Minh, Juan Carlos Caicedo, Stefanie Siegert, et al.. (2020). Objective assessment of stored blood quality by deep learning. Proceedings of the National Academy of Sciences. 117(35). 21381–21390. 67 indexed citations

Barnes, Claire M., et al.. (2019). Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events. Medicine & Science in Sports & Exercise. 52(1). 259–266. 2 indexed citations

Patterson, James O., Paul Rees, & Paul Nurse. (2019). Noisy Cell-Size-Correlated Expression of Cyclin B Drives Probabilistic Cell-Size Homeostasis in Fission Yeast. Current Biology. 29(8). 1379–1386.e4. 34 indexed citations

10.

Deng, Shuo, Lijie Li, & Paul Rees. (2019). Graphene/MoXY Heterostructures Adjusted by Interlayer Distance, External Electric Field, and Strain for Tunable Devices. ACS Applied Nano Materials. 2(6). 3977–3988. 70 indexed citations

11.

Wills, John W., et al.. (2019). Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media. Journal of Microscopy. 279(3). 177–184. 27 indexed citations

12.

Barnett, Chris J., et al.. (2018). Modifying the electrical properties of graphene by reversible point-ripple formation. Carbon. 143. 762–768. 15 indexed citations

13.

Summers, Huw D., Catherine A. Thornton, Shareen H. Doak, et al.. (2018). Investigating FlowSight® imaging flow cytometry as a platform to assess chemically induced micronuclei using human lymphoblastoid cells in vitro. Mutagenesis. 33(4). 283–289. 9 indexed citations

14.

Wills, John W., Huw D. Summers, Nicole Hondow, et al.. (2017). Characterizing Nanoparticles in Biological Matrices: Tipping Points in Agglomeration State and Cellular Delivery In Vitro. ACS Nano. 11(12). 11986–12000. 38 indexed citations

15.

Doan, Minh, Marian Case, Dino Mašić, et al.. (2017). Label-Free Analyses of Minimal Residual Disease in ALL Using Deep Learning and Imaging Flow Cytometry. Blood. 130. 1437–1437. 2 indexed citations

16.

Sheppard, Michael, et al.. (2017). Inter-Personal and Critical-Thinking Capabilities in Those about to Enter Qualified Social Work: A Six-Centre Study. The British Journal of Social Work. 48(7). 1855–1873. 19 indexed citations

17.

Denisov, D., et al.. (2015). The absolute calibration of high-precision optical flats across a wide range of spatial frequencies. Journal of Physics Conference Series. 584. 12020–12020. 3 indexed citations

18.

Rees, Paul. (2012). Student perspectives on groupwork. Groupwork. 19(1). 59–81.

19.

Walton, N. A., et al.. (1998). <title>Improving the effectiveness of 2-m-class telescopes through control systems redesign</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3351. 197–208.

20.

Summers, Huw D., Paul Rees, & P. Blood. (1993). TM and TE emission from strained AlGaInP visible emitting laser diodes. Conference on Lasers and Electro-Optics. 1 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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