Richard Archibald

2.6k total citations
92 papers, 1.8k citations indexed

About

Richard Archibald is a scholar working on Materials Chemistry, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Richard Archibald has authored 92 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Materials Chemistry, 16 papers in Artificial Intelligence and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Richard Archibald's work include Machine Learning in Materials Science (11 papers), Nuclear Physics and Applications (10 papers) and Probabilistic and Robust Engineering Design (9 papers). Richard Archibald is often cited by papers focused on Machine Learning in Materials Science (11 papers), Nuclear Physics and Applications (10 papers) and Probabilistic and Robust Engineering Design (9 papers). Richard Archibald collaborates with scholars based in United States, United Kingdom and China. Richard Archibald's co-authors include Anne Gelb, George I. Fann, Sergei V. Kalinin, Bobby G. Sumpter, Jungho Yoon, Stephen Jesse, Anthony M. Filippi, Dongbin Xiu, Rama K. Vasudevan and Alex Belianinov and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and Nature Materials.

In The Last Decade

Richard Archibald

80 papers receiving 1.7k citations

Peers

Richard Archibald

CVPR

EEE

AMPO

Peter Jansson Sweden

Ye Pu United States

L. Hesselink United States

Myungjoo Kang South Korea

Jonathan Blackledge United Kingdom

A. V. Goncharsky Russia

Yang Xiang China

E.R. Davies United Kingdom

Toru Ogawa Japan

Xiaodong Mu China

Peter Jansson Sweden

Richard Archibald

265 ×0.6

187 ×0.8

CVPR

178 ×0.8

EEE

185 ×0.8

AMPO

85 ×0.4

Citations per year, relative to Richard Archibald Richard Archibald (= 1×) peers Peter Jansson

Countries citing papers authored by Richard Archibald

Since Specialization

Citations

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

Fields of papers citing papers by Richard Archibald

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Richard Archibald

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

Chung, Matthias, Richard Archibald, Paul J. Atzberger, & J. Solomon. (2025). Sparse L^1-Autoencoders for Scientific Data Compression.

McDonnell, Marshall, et al.. (2024). Integrating scientific single-page applications with DevSecOps. Future Generation Computer Systems. 166. 107695–107695.

Archibald, Richard, et al.. (2024). Convergence Analysis for an Online Data-Driven Feedback Control Algorithm. Mathematics. 12(16). 2584–2584.

Archibald, Richard, et al.. (2024). StOKeDMD: Streaming Occupation kernel dynamic mode decomposition. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2(4). 433–464.

Archibald, Richard, Feng Bao, & Jiongmin Yong. (2023). A stochastic maximum principle approach for reinforcement learning with parameterized environment. Journal of Computational Physics. 488. 112238–112238. 3 indexed citations

Gong, Qian, Jieyang Chen, Ben Whitney, et al.. (2023). MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX. 24. 101590–101590. 18 indexed citations

Dyck, Ondrej, Feng Bao, Maxim Ziatdinov, et al.. (2022). Strain-Induced asymmetry and on-site dynamics of silicon defects in graphene. Carbon Trends. 9. 100189–100189. 1 indexed citations

Archibald, Richard, Feng Bao, Yanzhao Cao, & He Zhang. (2022). A backward SDE method for uncertainty quantification in deep learning. Discrete and Continuous Dynamical Systems - S. 15(10). 2807–2807. 1 indexed citations

Archibald, Richard & Hoang Tran. (2021). A dictionary learning algorithm for compression and reconstruction of streaming data in preset order. Discrete and Continuous Dynamical Systems - S. 15(4). 655–668.

10.

Sullivan, Brendan, Richard Archibald, Jahaun Azadmanesh, et al.. (2019). BraggNet: integrating Bragg peaks using neural networks. Journal of Applied Crystallography. 52(4). 854–863. 29 indexed citations

11.

Lin, Jiao, Richard Archibald, D. L. Abernathy, et al.. (2019). Super-resolution energy spectra from neutron direct-geometry spectrometers. Review of Scientific Instruments. 90(10). 6 indexed citations

12.

Sullivan, Brendan, et al.. (2019). Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis. PubMed. 15. 549–555. 5 indexed citations

13.

Somnath, Suhas, Kody J. H. Law, Anna N. Morozovska, et al.. (2018). Ultrafast current imaging by Bayesian inversion. Nature Communications. 9(1). 513–513. 14 indexed citations

14.

Sullivan, Brendan, Richard Archibald, Holger Dobbek, et al.. (2018). Improving the accuracy and resolution of neutron crystallographic data by three-dimensional profile fitting of Bragg peaks in reciprocal space. Acta Crystallographica Section D Structural Biology. 74(11). 1085–1095. 29 indexed citations

15.

Barnard, Richard, Hassina Bilheux, Todd J. Toops, et al.. (2018). Total variation-based neutron computed tomography. Review of Scientific Instruments. 89(5). 53704–53704. 7 indexed citations

16.

Bao, Feng, Richard Archibald, J. L. Niedziela, Dipanshu Bansal, & Olivier Delaire. (2016). Complex optimization for big computational and experimental neutron datasets. Nanotechnology. 27(48). 484002–484002. 3 indexed citations

17.

Belianinov, Alex, Rama K. Vasudevan, Evgheni Strelcov, et al.. (2015). Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets. PubMed. 1(1). 6–6. 86 indexed citations

18.

Kalinin, Sergei V., Bobby G. Sumpter, & Richard Archibald. (2015). Big–deep–smart data in imaging for guiding materials design. Nature Materials. 14(10). 973–980. 258 indexed citations

19.

White, James B., Richard Archibald, KJ Evans, & John B. Drake. (2011). Multiwavelet Discontinuous Galerkin Accelerated ELP Method for the Shallow Water Equations on the Cubed Sphere. Monthly Weather Review. 139(2). 1102–3. 1 indexed citations

20.

Archibald, Richard & Anne Gelb. (2002). A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity. IEEE Transactions on Medical Imaging. 21(4). 305–319. 97 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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