Thomas Burr

6 papers receiving 1.0k citations

Thomas Burr's Hit Papers

Pattern Recognition and Machine Learning 2008 · 792 citations
7920+6+12Years since publication250500750

Peers

Thomas Burr
Comparison fields: 5 of 157
  • Signal Processing 113
  • Artificial Intelligence 297
  • Computer Vision and Pattern Recognition 150
  • General Economics, Econometrics and Finance 46
  • Finance 50
Replace Jianhua Z. Huang with:
Jianhua Z. Huang United States
Antonio Cuevas Spain
Yoshinobu Kawahara Japan
Ta‐Hsin Li United States
Yiran Shen China
William Lefebvre France
Laurie Davies Germany
Xiaolin Li China
Jerome T. Connor United States
Hendrik P. Lopuhaä Netherlands
Thomas Burr relative to Jianhua Z. Huang United States Jianhua Z. Huang's profile →
Citations per field
00.5×1.5×
Jianhua Z. Huang · 1×
Citations per year

Countries citing papers authored by Thomas Burr

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Burr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 12 scholars most cited alongside Thomas Burr, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Thomas Burr Line = papers co-authored together Thomas Burr links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
Pattern Recognition and Machine Learning
Hit paper breakdown →
2008792
2 2007224
3 20207
4 20045
5 20212
6 20211
7 20230
8
Monte Carlo Modeling of the Californium-Interrogation with Prompt Neutron (CIPN) Device for Spent Nuclear Fuel Measurements
20140

About Thomas Burr

Thomas Burr is a scholar working on Radiation, Aerospace Engineering, Materials Chemistry, Signal Processing and Management Science and Operations Research, having authored 8 papers that have together received 1.0k indexed citations. Recurring topics across this work include Nuclear Physics and Applications (3 papers), Nuclear reactor physics and engineering (3 papers), Nuclear Materials and Properties (2 papers), Time Series Analysis and Forecasting (2 papers), Stock Market Forecasting Methods (2 papers), Anomaly Detection Techniques and Applications (1 paper), Magnetic confinement fusion research (1 paper) and Bone Tissue Engineering Materials (1 paper). The work is most often cited by research in Signal Processing (113 citations), Artificial Intelligence (297 citations), Computer Vision and Pattern Recognition (150 citations), General Economics, Econometrics and Finance (46 citations) and Finance (50 citations). Thomas Burr has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Andrea Favalli, Holly Trellue, J.S. Hendricks, Shraddha J. Vachhani, Siddhartha Pathak, D. Henzlová, William Charlton, John M. Finn, D. P. Brennan and Surya R. Kalidindi. Their work appears in journals such as Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Technometrics, Physics of Plasmas, JOM and Journal of the American Statistical Association.

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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