Thi-Bich-Hanh Dao
- Artificial Intelligence
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Computational Theory and Mathematics
- Co-authors
- Christel VrainThomas LampertPierre GançarskiGermain ForestierBruno CrémilleuxAlain ColmerauerThom FrühwirthS. S. Ravi
- Topics
- Data Management and Algorithms (4 papers)Logic, programming, and type systems (3 papers)Advanced Clustering Algorithms Research (3 papers)
- Journals
- Artificial IntelligenceIEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingData Mining and Knowledge Discovery
- Partner nations
- FranceGermanyUnited States
In The Last Decade
Thi-Bich-Hanh Dao
10 papers receiving 87 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 51
- Signal Processing 33
- Computer Vision and Pattern Recognition 21
- Computer Networks and Communications 13
- Computational Theory and Mathematics 12
Countries citing papers authored by Thi-Bich-Hanh Dao
This map shows the geographic impact of Thi-Bich-Hanh 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 Thi-Bich-Hanh Dao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thi-Bich-Hanh Dao more than expected).
Fields of papers citing papers by Thi-Bich-Hanh Dao
This network shows the impact of papers produced by Thi-Bich-Hanh 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 Thi-Bich-Hanh Dao. The network helps show where Thi-Bich-Hanh Dao may publish in the future.
Co-authorship network of co-authors of Thi-Bich-Hanh Dao
This figure shows the co-authorship network connecting the top 25 collaborators of Thi-Bich-Hanh Dao. A scholar is included among the top collaborators of Thi-Bich-Hanh Dao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thi-Bich-Hanh Dao. Thi-Bich-Hanh Dao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 11 | |
| 6 | 20 | |
| 7 | 3 | |
| 8 | 47 | |
| 9 | 2 | |
| 10 | Property grammar parsing seen as a constraint optimization problem | 1 |
| 11 | 2 | |
| 12 | 3 |
About Thi-Bich-Hanh Dao
Thi-Bich-Hanh Dao is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 93 indexed citations. Recurring topics across this work include Data Management and Algorithms (4 papers), Logic, programming, and type systems (3 papers) and Advanced Clustering Algorithms Research (3 papers). The work is most often cited by research in Signal Processing (33 citations), Computational Mathematics (1 citation) and Artificial Intelligence (51 citations). Thi-Bich-Hanh Dao has collaborated with scholars based in France, Germany and United States. Frequent co-authors include Christel Vrain, Thomas Lampert, Pierre Gançarski, Germain Forestier, Bruno Crémilleux, Alain Colmerauer, Thom Frühwirth, S. S. Ravi, Yannick Parmentier and Denys Duchier. Their work appears in journals such as Artificial Intelligence, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and Data Mining and Knowledge Discovery.
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.