Kumar Chellapilla

4.2k total citations
60 papers, 2.3k citations indexed

About

Kumar Chellapilla is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kumar Chellapilla has authored 60 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 15 papers in Information Systems and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kumar Chellapilla's work include Evolutionary Algorithms and Applications (21 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Spam and Phishing Detection (11 papers). Kumar Chellapilla is often cited by papers focused on Evolutionary Algorithms and Applications (21 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Spam and Phishing Detection (11 papers). Kumar Chellapilla collaborates with scholars based in United States, United Kingdom and Spain. Kumar Chellapilla's co-authors include David B. Fogel, Patrice Simard, Patrice Y. Simard, Gregory Buehrer, Mary Czerwinski, Kevin Larson, Gary B. Fogel, Wolf Kienzle, Reid Andersen and David Maxwell Chickering and has published in prestigious journals such as Proceedings of the IEEE, The Journal of the Acoustical Society of America and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Kumar Chellapilla

55 papers receiving 2.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kumar Chellapilla United States 23 1.2k 634 544 391 210 60 2.3k
G. Tesauro United States 15 1.1k 0.9× 530 0.8× 230 0.4× 174 0.4× 697 3.3× 22 2.2k
Danai Koutra United States 22 2.0k 1.6× 516 0.8× 592 1.1× 326 0.8× 793 3.8× 93 3.0k
Wesley W. Chu United States 31 1.1k 0.9× 509 0.8× 306 0.6× 839 2.1× 1.2k 5.8× 159 2.7k
Pak Chung Wong United States 22 511 0.4× 224 0.4× 1.1k 2.0× 400 1.0× 164 0.8× 79 1.8k
David Cohn United States 14 3.0k 2.4× 522 0.8× 711 1.3× 231 0.6× 228 1.1× 25 4.0k
Andrew Kachites McCallum United States 7 2.9k 2.3× 814 1.3× 739 1.4× 228 0.6× 195 0.9× 9 3.4k
Lingfei Wu United States 25 1.3k 1.0× 464 0.7× 290 0.5× 206 0.5× 238 1.1× 104 1.9k
Feng Hao United Kingdom 23 777 0.6× 1.1k 1.7× 1.0k 1.8× 733 1.9× 448 2.1× 99 2.2k
Daniel Lemire Canada 20 675 0.5× 647 1.0× 328 0.6× 390 1.0× 526 2.5× 75 1.5k
Serhii Havrylov Ukraine 3 1.4k 1.2× 346 0.5× 803 1.5× 229 0.6× 140 0.7× 6 2.4k

Countries citing papers authored by Kumar Chellapilla

Since Specialization
Citations

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

Fields of papers citing papers by Kumar Chellapilla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kumar Chellapilla

This figure shows the co-authorship network connecting the top 25 collaborators of Kumar Chellapilla. A scholar is included among the top collaborators of Kumar Chellapilla 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 Kumar Chellapilla. Kumar Chellapilla 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
1.
Chellapilla, Kumar. (2019). Building a Better Self-Driving Car. 3169–3169. 1 indexed citations
2.
Stokes, Jack W., Reid Andersen, Christian Seifert, & Kumar Chellapilla. (2010). WebCop: locating neighborhoods of malware on the web. 5–5. 31 indexed citations
3.
Castillo, Carlos, Kumar Chellapilla, & Dennis Fetterly. (2008). Proceedings of the 4th international workshop on Adversarial information retrieval on the web. 67–67. 3 indexed citations
4.
Castillo, Carlos, Kumar Chellapilla, & Brian D. Davison. (2007). AIRWeb 2007 : proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web, May 8, 2007, Banff, Alberta, Canada. Association for Computing Machinery eBooks. 11 indexed citations
5.
Chellapilla, Kumar, et al.. (2006). High Performance Convolutional Neural Networks for Document Processing. 268 indexed citations
6.
Chellapilla, Kumar & David Maxwell Chickering. (2006). Improving Cloaking Detection using Search Query Popularity and Monetizability.. 17–23. 33 indexed citations
7.
Chellapilla, Kumar & Patrice Simard. (2006). A New Radical Based Approach to Offline Handwritten East-Asian Character Recognition. 12 indexed citations
8.
Chellapilla, Kumar, Kevin Larson, Patrice Y. Simard, & Mary Czerwinski. (2005). Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs). 110 indexed citations
9.
Chellapilla, Kumar & Patrice Y. Simard. (2004). Using Machine Learning to Break Visual Human Interaction Proofs (HIPs). Neural Information Processing Systems. 17. 265–272. 165 indexed citations
10.
Shilman, Michael, Paul Viola, & Kumar Chellapilla. (2004). Recognition and Grouping of Handwritten Text in Diagrams and Equations. 19 indexed citations
11.
Chellapilla, Kumar, et al.. (2003). Evolving nonlinear time-series models using evolutionary programming. 236–243. 10 indexed citations
12.
Chellapilla, Kumar & David B. Fogel. (2003). Fitness distributions in evolutionary computation: analysis of local extrema in the continuous domain. 1885–1892. 3 indexed citations
14.
Chellapilla, Kumar & David B. Fogel. (2002). Anaconda defeats Hoyle 6-0: a case study competing an evolved checkers program against commercially available software. 2. 857–863. 35 indexed citations
15.
Chellapilla, Kumar & David B. Fogel. (2000). <title>Review of efforts to evolve strategies to play checkers as well as human experts</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4120. 43–51.
16.
Igel, Christian & Kumar Chellapilla. (1999). Investigating the influence of depth and degree of genotypic change on fitness in genetic programming. Genetic and Evolutionary Computation Conference. 1061–1068. 19 indexed citations
17.
Chellapilla, Kumar & David B. Fogel. (1999). Fitness distributions in evolutionary computation: motivation and examples in the continuous domain. Biosystems. 54(1-2). 15–29. 12 indexed citations
18.
Chellapilla, Kumar. (1998). Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation. 2(3). 91–96. 171 indexed citations
19.
Chellapilla, Kumar. (1997). Evolving computer programs without subtree crossover. IEEE Transactions on Evolutionary Computation. 1(3). 209–216. 73 indexed citations
20.
Chellapilla, Kumar & David B. Fogel. (1997). <title>Two new mutation operators for enhanced search and optimization in evolutionary programming</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3165. 260–269. 34 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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