Daewoo Pak
Impact in
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- Acute Myeloid Leukemia Research
- Statistics and Probability top 10%
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
Papers in
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- Statistical Methods and Bayesian Inference 4
- Statistical Methods and Inference 4
- Advanced Statistical Methods and Models 2
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- COVID-19 epidemiological studies 2
- Co-authors
- Zachary Frome Burton (3 shared papers)Chenxi Li (3 shared papers)David Todem (3 shared papers)Robert Scott Root-Bernstein (1 shared paper)Jing Ning (7 shared papers)Yanni Sun (1 shared paper)Nan Du (1 shared paper)Yu Shen (4 shared papers)
- Journals
- Transcription (3 papers)Statistics in Medicine (2 papers)Statistical Methods in Medical Research (2 papers)Lifetime Data Analysis (1 paper)Nutrients (1 paper)
- Partner nations
- United StatesSouth KoreaSpain
In The Last Decade
Daewoo Pak
22 papers receiving 273 citations
Peers
Comparison fields: 5 of 77
- Hematology 60
- Statistics and Probability 32
- Modeling and Simulation 12
- Molecular Biology 129
- Cancer Research 21
Countries citing papers authored by Daewoo Pak
This map shows the geographic impact of Daewoo Pak'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 Daewoo Pak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daewoo Pak more than expected).
Fields of papers citing papers by Daewoo Pak
This network shows the impact of papers produced by Daewoo Pak. 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 Daewoo Pak. The network helps show where Daewoo Pak may publish in the future.
Co-authors
The 25 scholars most cited alongside Daewoo Pak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 68 | |
| 2 | 2017 | 31 | |
| 3 | 2018 | 27 | |
| 4 | 2019 | 25 | |
| 5 | 2019 | 25 | |
| 6 | 2018 | 23 | |
| 7 | 2020 | 15 | |
| 8 | 2018 | 11 | |
| 9 | 2020 | 10 | |
| 10 | 2024 | 6 | |
| 11 | 2021 | 6 | |
| 12 | 2022 | 6 | |
| 13 | 2012 | 5 | |
| 14 | 2016 | 5 | |
| 15 | 2023 | 4 | |
| 16 | 2021 | 3 | |
| 17 | 2018 | 3 | |
| 18 | 2023 | 2 | |
| 19 | 2020 | 2 | |
| 20 | 2022 | 2 |
About Daewoo Pak
Daewoo Pak is a scholar working on Statistics and Probability, Modeling and Simulation, Infectious Diseases, Otorhinolaryngology and Periodontics, having authored 25 papers that have together received 281 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers), Genomics and Phylogenetic Studies (3 papers), RNA modifications and cancer (3 papers), RNA and protein synthesis mechanisms (3 papers), COVID-19 Clinical Research Studies (2 papers), COVID-19 epidemiological studies (2 papers) and Advanced Statistical Methods and Models (2 papers). The work is most often cited by research in Hematology (60 citations), Statistics and Probability (32 citations), Modeling and Simulation (12 citations), Molecular Biology (129 citations) and Cancer Research (21 citations). Daewoo Pak has collaborated with scholars based in United States, South Korea and Spain. Frequent co-authors include Zachary Frome Burton, Chenxi Li, David Todem, Robert Scott Root-Bernstein, Jing Ning, Yanni Sun, Nan Du, Yu Shen, Robert C. Bast and Karen H. Lu. Their work appears in journals such as Transcription, Statistics in Medicine, Statistical Methods in Medical Research, Lifetime Data Analysis and Nutrients.
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.