Peng Dai

766 total citations
25 papers, 456 citations indexed

About

Peng Dai is a scholar working on Artificial Intelligence, Computer Science Applications and Computational Theory and Mathematics. According to data from OpenAlex, Peng Dai has authored 25 papers receiving a total of 456 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 11 papers in Computer Science Applications and 7 papers in Computational Theory and Mathematics. Recurrent topics in Peng Dai's work include Mobile Crowdsensing and Crowdsourcing (11 papers), Data Stream Mining Techniques (8 papers) and Formal Methods in Verification (7 papers). Peng Dai is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (11 papers), Data Stream Mining Techniques (8 papers) and Formal Methods in Verification (7 papers). Peng Dai collaborates with scholars based in United States, China and Lithuania. Peng Dai's co-authors include Mausam Mausam, Daniel S. Weld, Ed H., Praveen Paritosh, Christopher H. Lin, Daniel S. Weld, Jeffrey M. Rzeszotarski, Judy Goldsmith, Eric A. Hansen and Shuo Chang and has published in prestigious journals such as Artificial Intelligence, Complexity and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Peng Dai

22 papers receiving 431 citations

Peers

Peng Dai
Comparison fields: 5 of 61
  • Computer Science Applications 299
  • Artificial Intelligence 256
  • Management Science and Operations Research 117
  • Computer Vision and Pattern Recognition 64
  • Information Systems 56
Pınar Dönmez United States
Caleb Chen Cao Hong Kong
Christopher H. Lin United States
Akash Das Sarma United States
Antonio Hernando Spain
Vibhor Kant India
Zhao Chen Hong Kong
Andy Edmonds Switzerland
Cen Chen China
Shengliang Xu China
Pınar Dönmez United States View profile →
Citations per field, relative to Peng Dai
Peng Dai · 1×
Citations per year, relative to Peng Dai
Peng Dai · 1×

Countries citing papers authored by Peng Dai

Since Specialization
Citations

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

Fields of papers citing papers by Peng Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Dai. A scholar is included among the top collaborators of Peng Dai 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 Peng Dai. Peng Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 1
2 17
3 13
4 10
5 47
6 3
7 81
8 1
9
Human intelligence needs artificial intelligence
19
10 47
11 4
12 3
13 98
14
Domain-independent, automatic partitioning for probabilistic planning
2
15
Event Based Dynamic Context Model for Group Interaction Analysis( Contribution to 21 Century Intelligent Technologies and Bioinformatics)
1
16
Partitioned external-memory value iteration
3
17
Prioritizing bellman backups without a priority queue
10
18 31
19 1
20 0

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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