Daniel Lee

481 citations
21 papers · 324 · h-index 7

Impact in

Papers in

Daniel Lee

20 papers receiving 318 citations

Peers

Daniel Lee
Comparison fields: 5 of 87
  • Automotive Engineering 73
  • Nuclear and High Energy Physics 79
  • Computer Vision and Pattern Recognition 83
  • Anesthesiology and Pain Medicine 13
  • Control and Systems Engineering 47
Replace Y. Hamada with:
Y. Hamada Japan
Yuji Sugimoto Japan
Dezheng Hua China
H. Gao China
Melissa L. McGuire United States
Lynton Ardizzone Germany
Matthias Heller Germany
D. Meziat Spain
A.D. Kanaris United States
C. Rapson Germany
Daniel Lee relative to Y. Hamada Japan Y. Hamada's profile →
Citations per field
00.5×4.3×
Y. Hamada · 1×
Citations per year

Countries citing papers authored by Daniel Lee

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008113
2 201889
3 200128
4 202027
5 202114
6
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss
201214
7 20119
8 20226
9 20233
10 20213
11 20133
12 20163
13
Optimal Neural Population Codes for High-dimensional Stimulus Variables
20132
14 20212
15 20232
16 20242
17 20101
18 20161
19 20251
20 20211

About Daniel Lee

Daniel Lee is a scholar working on Pulmonary and Respiratory Medicine, Public Health, Environmental and Occupational Health, Surgery, Anesthesiology and Pain Medicine and Molecular Biology, having authored 21 papers that have together received 324 indexed citations. Recurring topics across this work include Opioid Use Disorder Treatment (4 papers), Pain Management and Opioid Use (4 papers), Anesthesia and Pain Management (4 papers), Robotic Mechanisms and Dynamics (2 papers), Robotic Path Planning Algorithms (2 papers), Prostate Cancer Diagnosis and Treatment (2 papers), Prostate Cancer Treatment and Research (2 papers) and Neural dynamics and brain function (2 papers). The work is most often cited by research in Automotive Engineering (73 citations), Nuclear and High Energy Physics (79 citations), Computer Vision and Pattern Recognition (83 citations), Anesthesiology and Pain Medicine (13 citations) and Control and Systems Engineering (47 citations). Daniel Lee has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Dean Lee, Paul Vernaza, Ermal Rrapaj, Ilse C. F. Ipsen, John Spletzer, Jason Derenick, Alex Kushleyev, Tully Foote, J. Aislinn Bohren and Zhuo Wang. Their work appears in journals such as JAMA Network Open, Journal of Clinical Oncology, The Journal of Urology, Journal of Field Robotics and ACS Medicinal Chemistry Letters.

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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