J. Chan

21.6k citations
14 papers · 139 · h-index 6

Impact in

    • Olfactory and Sensory Function Studies
    • Particle physics theoretical and experimental studies
    • Particle Detector Development and Performance
    • High-Energy Particle Collisions Research

Papers in

J. Chan

13 papers receiving 135 citations

Peers

J. Chan
Comparison fields: 5 of 50
  • Sensory Systems 28
  • Nuclear and High Energy Physics 36
  • Artificial Intelligence 58
  • Computational Mathematics 1
  • Cellular and Molecular Neuroscience 26
Replace L. Xia with:
L. Xia China
F. Fratnik Italy
Daniele Bertolini United States
M. Richter Poland
Leonid Petrov United States
Sergei Gulyaev New Zealand
Elena S. Ackley United States
E. Ros Spain
Shuyang Cao United States
J. Michel France
J. Chan relative to L. Xia China L. Xia's profile →
Citations per field
00.5×10×13.5×
L. Xia · 1×
Citations per year

Countries citing papers authored by J. Chan

Since Specialization
Citations

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

Fields of papers citing papers by J. Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside J. Chan, 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 J. Chan Line = papers co-authored together J. Chan links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 202152
2 201537
3 202313
4 20238
5 20246
6 20195
7 20194
8 20174
9
Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
20193
10 20222
11 20032
12 20212
13 20251
14 20250

About J. Chan

J. Chan is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications, Artificial Intelligence, Molecular Biology and Cellular and Molecular Neuroscience, having authored 14 papers that have together received 139 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (9 papers), Particle Detector Development and Performance (6 papers), High-Energy Particle Collisions Research (5 papers), Distributed and Parallel Computing Systems (2 papers), Dark Matter and Cosmic Phenomena (1 paper), Neurobiology and Insect Physiology Research (1 paper), Dental Radiography and Imaging (1 paper) and Advanced machining processes and optimization (1 paper). The work is most often cited by research in Sensory Systems (28 citations), Nuclear and High Energy Physics (36 citations), Artificial Intelligence (58 citations), Computational Mathematics (1 citation) and Cellular and Molecular Neuroscience (26 citations). J. Chan has collaborated with scholars based in United States, Switzerland and Poland. Frequent co-authors include Benjamin Nachman, Miron Livny, A. DeSouza, W. Guan, Rui Hu, Richard S. Smith, Ricardo C. Araneda, Alberto Di Meglio, Federico Carminati and Samuel Yen-Chi Chen. Their work appears in journals such as Physical review. D, Biotechnology and Bioengineering, Journal of Physics G Nuclear and Particle Physics, Journal of Neuroscience and Journal of High Energy Physics.

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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