Samuel Ieong
- Information Systems top 1%
- Artificial Intelligence top 5%
- Management Science and Operations Research top 2%
- Signal Processing top 5%
- Economics and Econometrics top 5%
- Co-authors
- Sreenivas GollapudiRakesh AgrawalAlan HalversonYoav ShohamNina MishraQixiang SunEugene NudelmanEldar Sadikov
- Topics
- Consumer Market Behavior and Pricing (8 papers)Auction Theory and Applications (7 papers)Game Theory and Voting Systems (5 papers)
- Journals
- International Conference on Machine LearningNational Conference on Artificial IntelligenceACM SIGecom Exchanges
- Partner nations
- United StatesFranceIndia
In The Last Decade
Samuel Ieong
21 papers receiving 941 citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Information Systems 526
- Artificial Intelligence 387
- Management Science and Operations Research 285
- Signal Processing 184
- Economics and Econometrics 174
Countries citing papers authored by Samuel Ieong
This map shows the geographic impact of Samuel Ieong'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 Samuel Ieong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Ieong more than expected).
Fields of papers citing papers by Samuel Ieong
This network shows the impact of papers produced by Samuel Ieong. 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 Samuel Ieong. The network helps show where Samuel Ieong may publish in the future.
Co-authorship network of co-authors of Samuel Ieong
This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Ieong. A scholar is included among the top collaborators of Samuel Ieong 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 Samuel Ieong. Samuel Ieong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 12 | |
| 3 | 22 | |
| 4 | 22 | |
| 5 | Predicting Consumer Behavior in Commerce Search. | 4 |
| 6 | 38 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 12 | |
| 10 | 4 | |
| 11 | 5 | |
| 12 | 6 | |
| 13 | Bayesian coalitional games | 22 |
| 14 | Cooperation in competition: efficiently representing and reasoning about coalitional games | 1 |
| 15 | 19 | |
| 16 | Near-optimal search in continuous domains | 1 |
| 17 | 4 | |
| 18 | 18 | |
| 19 | Fast and compact: a simple class of congestion games | 60 |
| 20 | 123 |
About Samuel Ieong
Samuel Ieong is a scholar working on Marketing, Management Science and Operations Research and Computer Science Applications, having authored 21 papers that have together received 1.0k indexed citations. Recurring topics across this work include Consumer Market Behavior and Pricing (8 papers), Auction Theory and Applications (7 papers) and Game Theory and Voting Systems (5 papers). The work is most often cited by research in Information Systems (526 citations), Management Science and Operations Research (285 citations) and Signal Processing (184 citations). Samuel Ieong has collaborated with scholars based in United States, France and India. Frequent co-authors include Sreenivas Gollapudi, Rakesh Agrawal, Alan Halverson, Yoav Shoham, Nina Mishra, Qixiang Sun, Eugene Nudelman, Eldar Sadikov, Li Zhang and Isabelle Stanton. Their work appears in journals such as International Conference on Machine Learning, National Conference on Artificial Intelligence and ACM SIGecom Exchanges.
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.