Dan Gusfield

15.3k total citations · 4 hit papers
103 papers, 8.2k citations indexed

About

Dan Gusfield is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Dan Gusfield has authored 103 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 35 papers in Artificial Intelligence and 33 papers in Genetics. Recurrent topics in Dan Gusfield's work include Algorithms and Data Compression (29 papers), Genomics and Phylogenetic Studies (28 papers) and Complexity and Algorithms in Graphs (22 papers). Dan Gusfield is often cited by papers focused on Algorithms and Data Compression (29 papers), Genomics and Phylogenetic Studies (28 papers) and Complexity and Algorithms in Graphs (22 papers). Dan Gusfield collaborates with scholars based in United States, United Kingdom and Hong Kong. Dan Gusfield's co-authors include Robert W. Irving, Charles H. Langley, Susan Holmes, Jens Stoye, P.S.H. Leather, Dalit Naor, Charles U. Martel, Vineet Bafna, Lusheng Wang and Shibu Yooseph and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and Methods in enzymology on CD-ROM/Methods in enzymology.

In The Last Decade

Dan Gusfield

99 papers receiving 7.7k citations

Hit Papers

Algorithms on strings, trees, and sequences computer scie... 1991 2026 2002 2014 1997 1997 1997 1991 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dan Gusfield United States 34 3.9k 3.7k 1.5k 1.4k 1.1k 103 8.2k
Wolfgang Banzhaf Canada 36 4.6k 1.2× 1.8k 0.5× 1.1k 0.7× 426 0.3× 778 0.7× 231 7.7k
Stephanie Forrest United States 58 5.5k 1.4× 1.6k 0.4× 950 0.7× 539 0.4× 5.1k 4.7× 207 14.7k
Steven Skiena United States 37 2.1k 0.5× 1.6k 0.4× 498 0.3× 355 0.3× 447 0.4× 197 6.5k
Dennis Shasha United States 55 3.0k 0.8× 2.5k 0.7× 648 0.4× 237 0.2× 4.9k 4.6× 308 11.9k
Andrzej Ehrenfeucht United States 32 3.4k 0.9× 2.1k 0.6× 2.6k 1.8× 411 0.3× 442 0.4× 189 6.5k
Eli Upfal United States 42 2.3k 0.6× 968 0.3× 2.0k 1.4× 167 0.1× 4.3k 4.0× 176 8.2k
Jun Zhu China 42 4.8k 1.2× 2.0k 0.5× 325 0.2× 335 0.2× 323 0.3× 243 10.2k
V.I. Levenshtein Russia 22 4.3k 1.1× 1.4k 0.4× 751 0.5× 91 0.1× 996 0.9× 48 7.5k
Martı́n Farach-Colton United States 33 2.2k 0.6× 1.2k 0.3× 781 0.5× 276 0.2× 1.5k 1.4× 137 4.1k
Paolo Ferragina Italy 31 3.5k 0.9× 1.4k 0.4× 437 0.3× 80 0.1× 644 0.6× 135 4.8k

Countries citing papers authored by Dan Gusfield

Since Specialization
Citations

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

Fields of papers citing papers by Dan Gusfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Gusfield

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Gusfield. A scholar is included among the top collaborators of Dan Gusfield 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 Dan Gusfield. Dan Gusfield 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.
Gusfield, Dan, et al.. (2014). Faster algorithms for RNA-folding using the Four-Russians method. Algorithms for Molecular Biology. 9(1). 5–5. 10 indexed citations
2.
Gusfield, Dan, et al.. (2011). Extensions and Improvements to the Chordal Graph Approach to the Multistate Perfect Phylogeny Problem. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(4). 912–917. 4 indexed citations
3.
Gusfield, Dan, et al.. (2009). A simple, practical and complete O(n³/log n)-time algorithm for RNA folding using the four-Russians speedup. 97–107. 3 indexed citations
4.
Song, Yun S., Yufeng Wu, & Dan Gusfield. (2005). Efficient computation of close lower and upper bounds on the minimum number of recombinations in biological sequence evolution. Computer applications in the biosciences. 21(Suppl 1). i413–i422. 36 indexed citations
5.
Gusfield, Dan. (2005). Optimal, efficient reconstruction of root-unknown phylogenetic networks with constrained and structured recombination. Journal of Computer and System Sciences. 70(3). 381–398. 33 indexed citations
6.
Gusfield, Dan. (2004). RECOMB 2004 : proceedings of the eighth Annual International Conference on Research in Computational Molecular Biology, March 27-31, 2004, San Diego, California, USA. Association for Computing Machinery eBooks. 1 indexed citations
7.
Gusfield, Dan. (2003). An Overview of Haplotyping via Perfect Phylogeny: Theory, Algorithms and Programs. 18. 2 indexed citations
8.
9.
Stoye, Jens & Dan Gusfield. (2002). Simple and flexible detection of contiguous repeats using a suffix tree. Theoretical Computer Science. 270(1-2). 843–856. 47 indexed citations
10.
Wang, Lusheng, Tao Jiang, & Dan Gusfield. (2000). A More Efficient Approximation Scheme for Tree Alignment. SIAM Journal on Computing. 30(1). 283–299. 22 indexed citations
11.
Stoye, Jens & Dan Gusfield. (1998). Simple and flexible detection of contiguous repeats using a suffix tree. PUB – Publications at Bielefeld University (Bielefeld University). 1 indexed citations
12.
Gusfield, Dan, Richard M. Karp, Lusheng Wang, & Paul Stelling. (1998). Graph traversals, genes and matroids: An efficient case of the travelling salesman problem. Discrete Applied Mathematics. 88(1-3). 167–180. 5 indexed citations
13.
Kececioglu, John & Dan Gusfield. (1998). Reconstructing a history of recombinations from a set of sequences. Discrete Applied Mathematics. 88(1-3). 239–260. 27 indexed citations
14.
Wang, Lusheng & Dan Gusfield. (1998). Constructing Additive Trees When the Error Is Small. Journal of Computational Biology. 5(1). 127–133. 2 indexed citations
15.
Gusfield, Dan & Paul Stelling. (1996). [28] Parametric and inverse-parametric sequence alignment with XPARAL. Methods in enzymology on CD-ROM/Methods in enzymology. 266. 481–494. 24 indexed citations
16.
Kececioglu, John & Dan Gusfield. (1994). Reconstructing a history of recombinations from a set of sequences. Symposium on Discrete Algorithms. 471–480. 6 indexed citations
17.
Gusfield, Dan. (1993). Efficient methods for multiple sequence alignment with guaranteed error bounds. Bulletin of Mathematical Biology. 55(1). 141–154. 69 indexed citations
18.
Gusfield, Dan & Dalit Naor. (1991). Efficient algorithms for generalized cut‐trees. Networks. 21(5). 505–520. 5 indexed citations
19.
Gusfield, Dan & Dalit Naor. (1990). Efficient algorithms for generalized cut trees. Symposium on Discrete Algorithms. 422–433. 4 indexed citations
20.
Gusfield, Dan. (1981). A practical algorithm for optimal inference of haplotypes from diploid populations.. PubMed. 8. 183–9. 26 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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