M. Williams

5.8k total citations · 1 hit paper
24 papers, 724 citations indexed

About

M. Williams is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, M. Williams has authored 24 papers receiving a total of 724 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Nuclear and High Energy Physics, 3 papers in Artificial Intelligence and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in M. Williams's work include Particle physics theoretical and experimental studies (16 papers), High-Energy Particle Collisions Research (7 papers) and Particle Detector Development and Performance (7 papers). M. Williams is often cited by papers focused on Particle physics theoretical and experimental studies (16 papers), High-Energy Particle Collisions Research (7 papers) and Particle Detector Development and Performance (7 papers). M. Williams collaborates with scholars based in United States, Switzerland and United Kingdom. M. Williams's co-authors include Yotam Soreq, P. Ilten, Jesse Thaler, Wei Xue, Daniel Aloni, A. Aurisano, Alexander Radovic, M. Kagan, D. Rousseau and A. Himmel and has published in prestigious journals such as Nature, Physical Review Letters and SHILAP Revista de lepidopterología.

In The Last Decade

M. Williams

22 papers receiving 703 citations

Hit Papers

Machine learning at the energy and intensity frontiers of... 2018 2026 2020 2023 2018 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine learning at the energy and intensity frontiers of particle physics
    2018 253 citations Alexander Radovic, M. Williams et al. Nature profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Williams United States 10 573 127 85 67 43 24 724
T. Wongjirad United States 7 255 0.4× 44 0.3× 71 0.8× 85 1.3× 44 1.0× 10 463
A. Aurisano United States 6 245 0.4× 37 0.3× 92 1.1× 26 0.4× 45 1.0× 13 407
Ketan M. Patel India 18 893 1.6× 143 1.1× 22 0.3× 56 0.8× 73 1.7× 55 1.1k
T. Uckan United States 13 417 0.7× 221 1.7× 94 1.1× 54 0.8× 122 2.8× 71 614
A. Himmel United States 3 205 0.4× 21 0.2× 86 1.0× 26 0.4× 45 1.0× 4 356
K. Terao United States 7 165 0.3× 23 0.2× 84 1.0× 71 1.1× 100 2.3× 26 450
Wiesław A. Kamiński Poland 16 483 0.8× 31 0.2× 57 0.7× 61 0.9× 57 1.3× 59 667
D. Liu United States 13 564 1.0× 311 2.4× 41 0.5× 75 1.1× 69 1.6× 58 720
Andreas Maier Germany 24 1.2k 2.1× 386 3.0× 26 0.3× 90 1.3× 30 0.7× 71 1.5k
Denis Boyda Russia 12 204 0.4× 28 0.2× 95 1.1× 137 2.0× 18 0.4× 30 486

Countries citing papers authored by M. Williams

Since Specialization
Citations

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

Fields of papers citing papers by M. Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Williams

This figure shows the co-authorship network connecting the top 25 collaborators of M. Williams. A scholar is included among the top collaborators of M. Williams 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 M. Williams. M. Williams 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.
Williams, M., Zachary Faber, & Thomas J. Kelley. (2025). Comparison of artificial intelligence image processing with manual leucocyte differential to score immune cell infiltration in a mouse infection model of cystic fibrosis. Journal of Pathology Informatics. 17. 100438–100438. 1 indexed citations
2.
Hen, O., W. B. Li, Hongkai Liu, et al.. (2024). Probing axion-like particles at the Electron-Ion Collider. Journal of High Energy Physics. 2024(2). 15 indexed citations
3.
4.
Delaney, B., N. Schulte, G. Ciezarek, et al.. (2024). Applications of Lipschitz neural networks to the Run 3 LHCb trigger system. SHILAP Revista de lepidopterología. 295. 9005–9005.
5.
Kitouni, O., N. S. Nolte, & M. Williams. (2023). Robust and provably monotonic networks. Machine Learning Science and Technology. 4(3). 35020–35020. 5 indexed citations
6.
Ali, A., Fernando Barbosa, J. Bessuille, et al.. (2022). Initial performance of the GlueX DIRC detector. Journal of Physics Conference Series. 2374(1). 12009–12009. 1 indexed citations
7.
Aloni, Daniel, Yotam Soreq, & M. Williams. (2019). Coupling QCD-Scale Axionlike Particles to Gluons. Physical Review Letters. 123(3). 31803–31803. 73 indexed citations
8.
Aloni, Daniel, C. Fanelli, Yotam Soreq, & M. Williams. (2019). Photoproduction of Axionlike Particles. Physical Review Letters. 123(7). 71801–71801. 68 indexed citations
9.
Radovic, Alexander, M. Williams, D. Rousseau, et al.. (2018). Machine learning at the energy and intensity frontiers of particle physics. Nature. 560(7716). 41–48. 253 indexed citations breakdown →
10.
Williams, M.. (2017). A novel approach to the bias-variance problem in bump hunting. Journal of Instrumentation. 12(9). P09034–P09034. 8 indexed citations
11.
Ilten, P., Yotam Soreq, Jesse Thaler, M. Williams, & Wei Xue. (2016). Proposed Inclusive Dark Photon Search at LHCb. Physical Review Letters. 116(25). 251803–251803. 93 indexed citations
12.
Hardin, John & M. Williams. (2016). FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectors. Journal of Instrumentation. 11(10). P10007–P10007. 4 indexed citations
13.
Gligorov, V. V. & M. Williams. (2013). Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree. Journal of Instrumentation. 8(2). P02013–P02013. 52 indexed citations
14.
Williams, M.. (2011). ObservingCPviolation in many-body decays. Physical review. D. Particles, fields, gravitation, and cosmology. 84(5). 13 indexed citations
15.
Williams, M., et al.. (2011). The HLT2 Topological Lines. CERN Bulletin. 3 indexed citations
16.
Egede, U., J. Elmsheuser, Benjamin Gaidioz, et al.. (2010). User analysis of LHCbdata with Ganga. Journal of Physics Conference Series. 219(7). 72008–72008. 2 indexed citations
17.
Gershon, T. & M. Williams. (2009). Prospects for the measurement of the unitarity triangle angleγfromB0→DK+π−decays. Physical review. D. Particles, fields, gravitation, and cosmology. 80(9). 10 indexed citations
18.
Williams, M.. (2006). Drug Discovery Technologies. Current Protocols in Pharmacology. 35(1). 3 indexed citations
19.
Bishop, Deborah C., et al.. (2004). Vertical Conformance in a Mature Carbonate CO2 Flood: Salt Creek Field Unit, Texas. 5 indexed citations
20.
Day, Howard W. & M. Williams. (2000). JU2000 - Efficiency by Design. All Days. 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by T. Wongjirad Breakdown of academic impact, for papers by A. Aurisano Breakdown of academic impact, for papers by Ketan M. Patel Breakdown of academic impact, for papers by T. Uckan Breakdown of academic impact, for papers by A. Himmel Breakdown of academic impact, for papers by K. Terao Breakdown of academic impact, for papers by Wiesław A. Kamiński Breakdown of academic impact, for papers by D. Liu Breakdown of academic impact, for papers by Andreas Maier Breakdown of academic impact, for papers by Denis Boyda
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