Samuel Ieong

28 total papers · 1.6k total citations
21 papers, 1.0k citations indexed

About

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, Alan Halverson, Rakesh Agrawal, Yoav Shoham, Nina Mishra, Qixiang Sun, Eugene Nudelman, Li Zhang, Eldar Sadikov 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 940 citations

Hit Papers

Diversifying search results 2009 2026 2014 2020 2009 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Samuel Ieong 526 387 284 183 175 21 1.0k
Onno Zoeter 365 0.7× 370 1.0× 192 0.7× 88 0.5× 38 0.2× 27 912
David Maxwell Chickering 268 0.5× 696 1.8× 191 0.7× 113 0.6× 30 0.2× 35 1.1k
Paul Ogilvie 483 0.9× 628 1.6× 159 0.6× 142 0.8× 53 0.3× 23 973
Mark Montague 455 0.9× 424 1.1× 121 0.4× 252 1.4× 25 0.1× 15 885
Grant Schoenebeck 230 0.4× 516 1.3× 143 0.5× 92 0.5× 48 0.3× 43 917
Gavin Finnie 461 0.9× 348 0.9× 363 1.3× 38 0.2× 164 0.9× 61 1.1k
Amin Milani Fard 447 0.8× 159 0.4× 68 0.2× 121 0.7× 64 0.4× 35 804
William Webber 615 1.2× 655 1.7× 154 0.5× 134 0.7× 18 0.1× 35 1.2k
John Guiver 266 0.5× 556 1.4× 145 0.5× 102 0.6× 33 0.2× 27 1.1k
Noam Koenigstein 647 1.2× 513 1.3× 173 0.6× 223 1.2× 26 0.1× 67 1.1k

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).

Fields of papers citing papers by Samuel Ieong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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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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