Louis Feng

494 total citations
14 papers, 341 citations indexed

About

Louis Feng is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Louis Feng has authored 14 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Information Systems, 8 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Louis Feng's work include Software Engineering Research (4 papers), Recommender Systems and Techniques (4 papers) and Data Visualization and Analytics (3 papers). Louis Feng is often cited by papers focused on Software Engineering Research (4 papers), Recommender Systems and Techniques (4 papers) and Data Visualization and Analytics (3 papers). Louis Feng collaborates with scholars based in United States and United Kingdom. Louis Feng's co-authors include Jonathan I. Maletic, Andrian Marcus, Kenneth I. Joy, Brian Budge, Sven Woop, Carsten Benthin, Ingo Wald, Arun Kejariwal, John D. Owens and Changkyu Kim and has published in prestigious journals such as ACM Transactions on Graphics, IEEE Transactions on Parallel and Distributed Systems and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

In The Last Decade

Louis Feng

13 papers receiving 313 citations

Author Peers

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

Author Last Decade Papers Cites
Louis Feng 176 171 113 90 72 14 341
Thierry Priol 132 0.8× 147 0.9× 140 1.2× 76 0.8× 52 0.7× 72 525
Stuart Sechrest 167 0.9× 60 0.4× 43 0.4× 26 0.3× 86 1.2× 34 767
Rikio Onai 97 0.6× 53 0.3× 32 0.3× 14 0.2× 90 1.3× 33 270
Linlin Chen 44 0.3× 115 0.7× 29 0.3× 33 0.4× 276 3.8× 14 398
Francisco Botana 13 0.1× 24 0.1× 41 0.4× 74 0.8× 56 0.8× 24 265
Isaac Chao 48 0.3× 34 0.2× 130 1.2× 171 1.9× 154 2.1× 14 413
Micah Goldblum 147 0.8× 29 0.2× 18 0.2× 15 0.2× 231 3.2× 21 386
Emilio Di Giacomo 169 1.0× 39 0.2× 236 2.1× 6 0.1× 34 0.5× 66 395
Yuanwei Zhu 66 0.4× 89 0.5× 7 0.1× 9 0.1× 100 1.4× 10 286
Hiroshi Hosobe 34 0.2× 15 0.1× 8 0.1× 13 0.1× 42 0.6× 40 139

Countries citing papers authored by Louis Feng

Since Specialization
Citations

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

Fields of papers citing papers by Louis Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Louis Feng

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

All Works

14 of 14 papers shown
1.
Sun, Ning, et al.. (2024). Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms. IEEE Transactions on Parallel and Distributed Systems. 36(2). 226–238.
2.
Feng, Louis, Ehsan K. Ardestani, Jaewon Lee, et al.. (2022). Building a Performance Model for Deep Learning Recommendation Model Training on GPUs. 227–229. 4 indexed citations
3.
Zha, Daochen, et al.. (2022). AutoShard: Automated Embedding Table Sharding for Recommender Systems. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4461–4471. 12 indexed citations
4.
Feng, Louis, et al.. (2022). Building a Performance Model for Deep Learning Recommendation Model Training on GPUs. 48–58. 6 indexed citations
5.
Yu, Jiecao, Tianxiang Gao, Louis Feng, et al.. (2021). Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). 1421–1428. 2 indexed citations
6.
Woop, Sven, Louis Feng, Ingo Wald, & Carsten Benthin. (2013). Embree ray tracing kernels for CPUs and the Xeon Phi architecture. 1–1. 20 indexed citations
7.
Feng, Louis, et al.. (2006). MatDL: Integrating Digital Libraries into Scientific Practice. Texas Digital Library (University of Texas). 5(3). 5 indexed citations
8.
Budge, Brian, et al.. (2005). Shell maps. 626–633. 18 indexed citations
9.
Budge, Brian, et al.. (2005). Shell maps. ACM Transactions on Graphics. 24(3). 626–633. 91 indexed citations
10.
Maletic, Jonathan I., Andrian Marcus, & Louis Feng. (2003). Source Viewer 3D (sv3D) - a framework for software visualization. International Conference on Software Engineering. 812–813. 13 indexed citations
11.
Marcus, Andrian, Louis Feng, & Jonathan I. Maletic. (2003). Source Viewer 3D (sv3D): A System for Visualizing Multi Dimensional Software Analysis Data.. 62–63. 1 indexed citations
12.
Marcus, Andrian, Louis Feng, & Jonathan I. Maletic. (2003). 3D representations for software visualization. 55 indexed citations
13.
Marcus, Andrian, Louis Feng, & Jonathan I. Maletic. (2003). 3D representations for software visualization. 27–27. 113 indexed citations
14.
Melton, Austin, et al.. (2002). Effectiveness of tagging laboratory data using Dublin Core in an electronic scientific notebook. euroCRIS DSpace CRIS digital repository (The International Organisation for Research Information). 1 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.

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