Ming Dao

25.4k citations
192 papers · 20.2k indexed · 15 hit papers · h-index 68

Ming Dao

191 papers receiving 19.7k citations

Hit Papers

Analyses of ...22120012026200920174008001.2k

Peers

Ming Dao
Comparison fields: 5 of 196
  • Mechanics of Materials 4.6k
  • Mechanical Engineering 5.8k
  • Biomedical Engineering 5.6k
  • Materials Chemistry 5.9k
  • Physiology 3.1k
Replace Gang Bao with:
Gang Bao United States
Subra Suresh United States
S. Suresh United States
Tian Jian Lu China
Roderic S. Lakes United States
Mary C. Boyce United States
A. W. Neumann Canada
Chwee Teck Lim Singapore
Hans‐Jürgen Butt Germany
Yaming Wang China
Ming Dao relative to Gang Bao United States Gang Bao's profile →
Citations per field
00.5×3.2×
Gang Bao · 1×
Citations per year

Countries citing papers authored by Ming Dao

Since Specialization
Citations

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

Fields of papers citing papers by Ming Dao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20248
2 202411
3 20237
4 20234
5 202326
6 20234
7 20234
8
Analyses of internal structures and defects in materials using physics-informed neural networksbreakdown →
2022221
9 202128
10 202134
11 202133
12 202115
13
Extraction of mechanical properties of materials through deep learning from instrumented indentationbreakdown →
2020242
14 202035
15 201955
16 201983
17 2018101
18 201862
19 201852
20 201290

About Ming Dao

Ming Dao is a scholar working on Mechanics of Materials, Physiology and Genetics, having authored 192 papers that have together received 20.2k indexed citations. Recurring topics across this work include Erythrocyte Function and Pathophysiology (45 papers), Blood properties and coagulation (43 papers), Microstructure and mechanical properties (39 papers), Metal and Thin Film Mechanics (38 papers), Aluminum Alloys Composites Properties (23 papers), Hemoglobinopathies and Related Disorders (20 papers), Malaria Research and Control (17 papers) and Force Microscopy Techniques and Applications (15 papers). The work is most often cited by research in Mechanics of Materials (4.6k citations), Mechanical Engineering (5.8k citations) and Biomedical Engineering (5.6k citations). Ming Dao has collaborated with scholars based in United States, Singapore and China. Frequent co-authors include S. Suresh, Subra Suresh, Lei Lu, Chwee Teck Lim, Nuwong Chollacoop, R.J. Asaro, Ju Li, Subra Suresh, George Em Karniadakis and T. A. Venkatesh. Their work appears in journals such as Proceedings of the National Academy of Sciences, Acta Materialia, Biophysical Journal, Scientific Reports and Scripta Materialia.

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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