Thomas D. Barrett

465 total citations
10 papers, 237 citations indexed

About

Thomas D. Barrett is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Molecular Biology. According to data from OpenAlex, Thomas D. Barrett has authored 10 papers receiving a total of 237 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Atomic and Molecular Physics, and Optics and 2 papers in Molecular Biology. Recurrent topics in Thomas D. Barrett's work include Neural Networks and Reservoir Computing (3 papers), Machine Learning in Bioinformatics (2 papers) and Protein Structure and Dynamics (2 papers). Thomas D. Barrett is often cited by papers focused on Neural Networks and Reservoir Computing (3 papers), Machine Learning in Bioinformatics (2 papers) and Protein Structure and Dynamics (2 papers). Thomas D. Barrett collaborates with scholars based in United Kingdom, Spain and Russia. Thomas D. Barrett's co-authors include A. I. Lvovsky, William R. Clements, Jakob Foerster, Xianxin Guo, Zhiming Wang, Axel Kuhn, Timothy Atkinson, Matthew Greenig, C.C. Tan and Zhiming M. Wang and has published in prestigious journals such as Physical Review Letters, Nature Communications and Bioinformatics.

In The Last Decade

Thomas D. Barrett

10 papers receiving 225 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thomas D. Barrett United Kingdom	5	125	74	47	35	29	10	237
Mahboobeh Houshmand Iran	10	183 1.5×	31 0.4×	52 1.1×	26 0.7×	16 0.6×	40	280
Rahmat Mulyawan Indonesia	10	61 0.5×	238 3.2×	11 0.2×	34 1.0×	11 0.4×	50	374
Soheil Salehi United States	11	96 0.8×	211 2.9×	75 1.6×	50 1.4×	5 0.2×	56	373
Ibrahim Ahmed United States	8	171 1.4×	207 2.8×	66 1.4×	36 1.0×	4 0.1×	17	320
Bertrand Cambou United States	9	50 0.4×	95 1.3×	12 0.3×	25 0.7×	7 0.2×	42	192
Shouzhen Gu China	11	85 0.7×	143 1.9×	14 0.3×	148 4.2×	7 0.2×	29	317
Christian Hirsch Germany	9	30 0.2×	54 0.7×	11 0.2×	19 0.5×	4 0.1×	56	243
Hiromi Miyajima Japan	8	124 1.0×	102 1.4×	9 0.2×	55 1.6×	4 0.1×	101	276

Countries citing papers authored by Thomas D. Barrett

Since Specialization

Citations

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

Fields of papers citing papers by Thomas D. Barrett

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas D. Barrett. 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 Thomas D. Barrett. The network helps show where Thomas D. Barrett may publish in the future.

Co-authorship network of co-authors of Thomas D. Barrett

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

Atkinson, Timothy, Thomas D. Barrett, Scott Cameron, et al.. (2025). Protein sequence modelling with Bayesian flow networks. Nature Communications. 16(1). 3197–3197. 1 indexed citations

Barrett, Thomas D., et al.. (2023). Bursts of polarised single photons from atom-cavity sources. Journal of Physics B Atomic Molecular and Optical Physics. 56(20). 205003–205003. 2 indexed citations

Barrett, Thomas D., et al.. (2023). Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4061–4069. 11 indexed citations

Barrett, Thomas D., et al.. (2022). ManyFold: an efficient and flexible library for training and validating protein folding models. Bioinformatics. 39(1). 1 indexed citations

Barrett, Thomas D., et al.. (2022). Autoregressive neural-network wavefunctions for ab initio quantum chemistry. Nature Machine Intelligence. 4(4). 351–358. 51 indexed citations

Guo, Xianxin, Thomas D. Barrett, Zhiming Wang, & A. I. Lvovsky. (2021). Backpropagation through nonlinear units for the all-optical training of neural networks. Photonics Research. 9(3). B71–B71. 63 indexed citations

Barrett, Thomas D., et al.. (2020). Learning Disentangled Representations and Group Structure of Dynamical Environments. Neural Information Processing Systems. 33. 19727–19737. 4 indexed citations

Barrett, Thomas D., et al.. (2020). Exploratory Combinatorial Optimization with Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 3243–3250. 94 indexed citations

Guo, Xianxin, Thomas D. Barrett, Zhiming M. Wang, & A. I. Lvovsky. (2019). End-to-end optical backpropagation for training neural networks.. arXiv (Cornell University). 1 indexed citations

10.

Barrett, Thomas D., et al.. (2019). Polarization Oscillations in Birefringent Emitter-Cavity Systems. Physical Review Letters. 122(8). 83602–83602. 9 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.

Explore authors with similar magnitude of impact