Jin K. Kim

2.1k citations
138 papers · 1.3k indexed · h-index 18

Impact in

    • Artificial Intelligence in Healthcare and Education
  • Urology top 2%
    • Urological Disorders and Treatments

Papers in

Jin K. Kim

122 papers receiving 1.3k citations

Peers

Jin K. Kim
Comparison fields: 5 of 147
  • Health Informatics 94
  • Urology 222
  • Nephrology 113
  • Pediatrics, Perinatology and Child Health 249
  • Transplantation 29
Replace Hooshang Kangarloo with:
Hooshang Kangarloo United States
Arkadiusz Miernik Germany
Randall C. Wetzel United States
Matthew Li United States
Sachin Agarwal United States
Geoffrey A. Sonn United States
Petros Martirosian Germany
Bekir Çakır Türkiye
Michael Lieber United States
Lorenzo Nardo United States
Jin K. Kim relative to Hooshang Kangarloo United States Hooshang Kangarloo's profile →
Citations per field
00.5×10×15×
Hooshang Kangarloo · 1×
Citations per year

Countries citing papers authored by Jin K. Kim

Since Specialization
Citations

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

Fields of papers citing papers by Jin K. Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20250
4 20250
5 20254
6 20251
7 20231
8 20237
9 20221
10 20222
11 20217
12 202132
13 20205
14 201913
15
STRADS-AP: Simplifying Distributed Machine Learning Programming without Introducing a New Programming Model.
20192
16 20195
17 20190
18 20196
19
Aging Behavior of Natural Rubber and EPDM
19981
20 199533

About Jin K. Kim

Jin K. Kim is a scholar working on Urology, Health Informatics, Transplantation, Pediatrics, Perinatology and Child Health and Emergency Medicine, having authored 138 papers that have together received 1.3k indexed citations. Recurring topics across this work include Pediatric Urology and Nephrology Studies (48 papers), Urological Disorders and Treatments (40 papers), Urinary Bladder and Prostate Research (14 papers), Urinary Tract Infections Management (13 papers), Semiconductor Quantum Structures and Devices (13 papers), Advanced Semiconductor Detectors and Materials (12 papers), Renal Transplantation Outcomes and Treatments (11 papers) and Renal and Vascular Pathologies (8 papers). The work is most often cited by research in Health Informatics (94 citations), Urology (222 citations), Nephrology (113 citations), Pediatrics, Perinatology and Child Health (249 citations) and Transplantation (29 citations). Jin K. Kim has collaborated with scholars based in Canada, United States and Philippines. Frequent co-authors include Michael Chua, Armando J. Lorenzo, Mandy Rickard, Robert W. Schrier, Sandra N. Summer, Jessica M. Ming, Joana Dos Santos, Martin A. Koyle, John F. Klem and Samuel D. Hawkins. Their work appears in journals such as Journal of Pediatric Urology, World Journal of Urology, The Journal of Urology, British Journal of Urology and Pediatric Transplantation.

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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