K. Pedro

47.1k total citations
17 papers, 146 citations indexed

About

K. Pedro is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Radiation. According to data from OpenAlex, K. Pedro has authored 17 papers receiving a total of 146 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Nuclear and High Energy Physics, 4 papers in Astronomy and Astrophysics and 4 papers in Radiation. Recurrent topics in K. Pedro's work include Particle physics theoretical and experimental studies (12 papers), Particle Detector Development and Performance (11 papers) and Radiation Detection and Scintillator Technologies (4 papers). K. Pedro is often cited by papers focused on Particle physics theoretical and experimental studies (12 papers), Particle Detector Development and Performance (11 papers) and Radiation Detection and Scintillator Technologies (4 papers). K. Pedro collaborates with scholars based in United States, Switzerland and Italy. K. Pedro's co-authors include O. Amram, M. Pierini, F. Canelli, L. T. Le Pottier, A. De Cosa, J. Niedziela, S. Summers, Nhan Viet Tran, S. Jindariani and Dylan Rankin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of High Energy Physics and Physical review. D.

In The Last Decade

K. Pedro

15 papers receiving 142 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
K. Pedro United States 7 83 49 34 19 18 17 146
Engin Eren Germany 7 210 2.5× 80 1.6× 38 1.1× 8 0.4× 11 0.6× 11 266
L. Gouskos Switzerland 3 157 1.9× 58 1.2× 14 0.4× 8 0.4× 10 0.6× 5 197
J. A. Raine Switzerland 10 142 1.7× 59 1.2× 15 0.4× 8 0.4× 6 0.3× 19 179
T. K. Aarrestad Switzerland 6 83 1.0× 59 1.2× 22 0.6× 5 0.3× 18 1.0× 8 148
W. Korcari Germany 5 91 1.1× 33 0.7× 19 0.6× 4 0.2× 4 0.2× 7 119
Ekaterina Govorkova United States 6 52 0.6× 46 0.9× 9 0.3× 15 0.8× 7 0.4× 9 99
Richard Y. Chen United States 6 13 0.2× 37 0.8× 23 0.7× 8 0.4× 8 0.4× 10 152
B. S. Rao India 9 78 0.9× 14 0.3× 33 1.0× 75 3.9× 19 1.1× 27 179
G. Quétant Switzerland 4 46 0.6× 17 0.3× 11 0.3× 6 0.3× 5 0.3× 7 71

Countries citing papers authored by K. Pedro

Since Specialization
Citations

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

Fields of papers citing papers by K. Pedro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Pedro

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

All Works

17 of 17 papers shown
1.
Savard, C., N. Manganelli, B. Holzman, et al.. (2024). Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server. CU Scholar (University of Colorado Boulder). 8(1). 4 indexed citations
2.
Srimanobhas, N., Sw. Banerjee, Vladimir Ivantchenko, et al.. (2024). Full Simulation of CMS for Run-3 and Phase-2. SHILAP Revista de lepidopterología. 295. 3017–3017.
3.
Bein, Samuel, Patrick Connor, K. Pedro, P. Schleper, & M. Wolf. (2024). Refining fast simulation using machine learning. SHILAP Revista de lepidopterología. 295. 9032–9032. 1 indexed citations
4.
Cai, T., K. Herner, T. Yang, et al.. (2023). Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing. PubMed. 7(1). 11–11. 2 indexed citations
5.
Pedro, K., et al.. (2023). DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. Machine Learning Science and Technology. 4(2). 25013–25013. 8 indexed citations
6.
Banerjee, S., et al.. (2023). Denoising Convolutional Networks to Accelerate Detector Simulation. Journal of Physics Conference Series. 2438(1). 12079–12079. 3 indexed citations
7.
Pedro, K., et al.. (2023). Optimal mass variables for semivisible jets. SciPost Physics Core. 6(4). 3 indexed citations
8.
Pedro, K., et al.. (2023). Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
9.
Amram, O. & K. Pedro. (2023). Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation. Physical review. D. 108(7). 24 indexed citations
10.
Snyder, Gregory F., Javier Sánchez, Gabriel Perdue, et al.. (2022). DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. Machine Learning Science and Technology. 3(3). 35007–35007. 14 indexed citations
11.
Apostolakis, J., Marilena Bandieramonte, Sw. Banerjee, et al.. (2022). Detector Simulation Challenges for Future Accelerator Experiments. Frontiers in Physics. 10. 6 indexed citations
12.
Canelli, F., A. De Cosa, L. T. Le Pottier, et al.. (2022). Autoencoders for semivisible jet detection. Journal of High Energy Physics. 2022(2). 23 indexed citations
13.
Ivanchenko, V., Sw. Banerjee, G. Hugo, et al.. (2021). CMS Full Simulation for Run 3. SHILAP Revista de lepidopterología. 251. 3016–3016. 3 indexed citations
14.
Ngadiuba, J., Vladimir Lončar, M. Pierini, et al.. (2020). Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. DSpace@MIT (Massachusetts Institute of Technology). 44 indexed citations
15.
Pedro, K.. (2019). Integration and Performance of New Technologies in the CMS Simulation. Springer Link (Chiba Institute of Technology). 4 indexed citations
16.
Pedro, K.. (2019). Current and Future Performance of the CMS Simulation. SHILAP Revista de lepidopterología. 214. 2036–2036. 6 indexed citations
17.
Hildreth, M., E. Sexton-Kennedy, K. Pedro, & M. J. Kortelainen. (2018). Strategies for Modeling Extreme Luminosities in the CMS Simulation. 347–347.

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 Engin Eren Breakdown of academic impact, for papers by L. Gouskos Breakdown of academic impact, for papers by J. A. Raine Breakdown of academic impact, for papers by T. K. Aarrestad Breakdown of academic impact, for papers by W. Korcari Breakdown of academic impact, for papers by Ekaterina Govorkova Breakdown of academic impact, for papers by Richard Y. Chen Breakdown of academic impact, for papers by B. S. Rao Breakdown of academic impact, for papers by G. Quétant
Rankless by CCL
2026