Samuel Ieong is a scholar working on Management Science and Operations Research, Artificial Intelligence and Marketing.
According to data from OpenAlex, Samuel Ieong has authored 21 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Management Science and Operations Research, 9 papers in Artificial Intelligence and 8 papers in Marketing. Recurrent topics in Samuel Ieong's work include Consumer Market Behavior and Pricing (8 papers), Auction Theory and Applications (7 papers) and Game Theory and Voting Systems (5 papers). Samuel Ieong is often cited by papers focused on Consumer Market Behavior and Pricing (8 papers), Auction Theory and Applications (7 papers) and Game Theory and Voting Systems (5 papers). Samuel Ieong collaborates with scholars based in United States, France and India. Samuel Ieong's co-authors include Sreenivas Gollapudi, Rakesh Agrawal, Alan Halverson, Yoav Shoham, Nina Mishra, Qixiang Sun, Eugene Nudelman, Eldar Sadikov, Li Zhang and Isabelle Stanton and has published in prestigious journals such as International Conference on Machine Learning, National Conference on Artificial Intelligence and ACM SIGecom Exchanges.
In The Last Decade
Samuel Ieong
21 papers
receiving
941 citations
Hit Papers
What are hit papers?
Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Diversifying search results
2009629 citationsSreenivas Gollapudi, Samuel Ieong et al.profile →
Citations per year, relative to Samuel Ieong Samuel Ieong (= 1×)
peers
Gianluigi Greco
Countries citing papers authored by Samuel Ieong
Since
Specialization
Citations
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).
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.
Agrawal, Rakesh, Samuel Ieong, & Raja P. Velu. (2011). Timing when to buy. 709–718.6 indexed citations
13.
Ieong, Samuel & Yoav Shoham. (2008). Bayesian coalitional games. National Conference on Artificial Intelligence. 95–100.22 indexed citations
14.
Shoham, Yoav, Ashish Goel, Tim Roughgarden, & Samuel Ieong. (2008). Cooperation in competition: efficiently representing and reasoning about coalitional games.1 indexed citations
Ieong, Samuel, Nicolas Lambert, Yoav Shoham, & Ronen I. Brafman. (2007). Near-optimal search in continuous domains. National Conference on Artificial Intelligence. 1158–1163.1 indexed citations
Ieong, Samuel, et al.. (2005). Fast and compact: a simple class of congestion games. National Conference on Artificial Intelligence. 489–494.60 indexed citations
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.