Dakuo Wang

89 papers receiving 2.3k citations

Hit Papers

Mental-LLM 2024 · 78 citations
78202420262025255075

Peers

Dakuo Wang
Comparison fields: 5 of 138
  • Health Informatics 240
  • Human-Computer Interaction 310
  • Computer Science Applications 232
  • Information Systems and Management 284
  • Safety Research 310
Replace Carrie J. Cai with:
Carrie J. Cai United States
Jina Suh United States
Besmira Nushi United States
Q. Vera Liao United States
Michael Terry Canada
Mihaela Vorvoreanu United States
Adam Fourney United States
Paul N. Bennett United States
Deborah Richards Australia
Dakuo Wang relative to Carrie J. Cai United States Carrie J. Cai's profile →
Citations per field
00.5×1.7×
Carrie J. Cai · 1×
Citations per year

Countries citing papers authored by Dakuo Wang

Since Specialization
Citations

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

Fields of papers citing papers by Dakuo Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 20252
4 20242
5 20242
6
Mental-LLM
Hit paper breakdown →
202478
7 20244
8 202435
9 202410
10 202318
11 20239
12 20234
13 20224
14 202214
15 20225
16
How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks.
20214
17 202147
18 202092
19
A Formal Method for AutoML via ADMM
20191
20 201915

About Dakuo Wang

Dakuo Wang is a scholar working on Health Informatics, Computer Science Applications, Artificial Intelligence, Information Systems and Management and Human-Computer Interaction, having authored 95 papers that have together received 2.4k indexed citations. Recurring topics across this work include Topic Modeling (22 papers), AI in Service Interactions (13 papers), Natural Language Processing Techniques (10 papers), Software Engineering Research (9 papers), Explainable Artificial Intelligence (XAI) (9 papers), Ethics and Social Impacts of AI (8 papers), Scientific Computing and Data Management (8 papers) and Speech and dialogue systems (7 papers). The work is most often cited by research in Health Informatics (240 citations), Human-Computer Interaction (310 citations), Computer Science Applications (232 citations), Information Systems and Management (284 citations) and Safety Research (310 citations). Dakuo Wang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Michael Müller, Xiangmin Fan, Casey Dugan, Amy X. Zhang, Zhan Zhang, Bingsheng Yao, Thomas Erickson, Mark Warschauer, Feng Tian and Philipp Geyer. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, ACM Transactions on Computer-Human Interaction, Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, Journal of Medical Internet Research and IEEE Journal of Biomedical and Health Informatics.

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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