Dan Kalman

1.7k citations
66 papers · 1.1k indexed · h-index 13

Impact in

Papers in

Dan Kalman

56 papers receiving 957 citations

Peers

Dan Kalman
Comparison fields: 5 of 124
  • Theoretical Computer Science 56
  • Algebra and Number Theory 124
  • Computational Mathematics 13
  • Discrete Mathematics and Combinatorics 70
  • Statistical and Nonlinear Physics 265
Replace Michael Monagan with:
Michael Monagan Canada
K. O. Geddes Canada
Srishti D. Chatterji Switzerland
Frank Uhlig United States
Joseph P. S. Kung United States
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Alexander Ostrowski Switzerland
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Dan Kalman relative to Michael Monagan Canada Michael Monagan's profile →
Citations per field
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Michael Monagan · 1×
Citations per year

Countries citing papers authored by Dan Kalman

Since Specialization
Citations

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

Fields of papers citing papers by Dan Kalman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 7 scholars most cited alongside Dan Kalman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Kalman Line = papers co-authored together Dan Kalman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20230
2 20223
3 200917
4 200824
5 20074
6 20071
7 20051
8 200122
9 199712
10 1996141
11 19953
12 19908
13 19862
14
Up Fractions! Up n divided by m!.
19851
15 19851
16 19851
17 198421
18 19821
19 198253
20 19805

About Dan Kalman

Dan Kalman is a scholar working on Theoretical Computer Science, Geometry and Topology, Algebra and Number Theory, Computational Theory and Mathematics and Numerical Analysis, having authored 66 papers that have together received 1.1k indexed citations. Recurring topics across this work include Mathematics and Applications (19 papers), History and Theory of Mathematics (12 papers), Matrix Theory and Algorithms (8 papers), Mathematics Education and Teaching Techniques (6 papers), Advanced Mathematical Theories and Applications (6 papers), Polynomial and algebraic computation (5 papers), Advanced Mathematical Theories (3 papers) and Numerical Methods and Algorithms (3 papers). The work is most often cited by research in Theoretical Computer Science (56 citations), Algebra and Number Theory (124 citations), Computational Mathematics (13 citations), Discrete Mathematics and Combinatorics (70 citations) and Statistical and Nonlinear Physics (265 citations). Dan Kalman has collaborated with scholars based in United States. Frequent co-authors include James E. White, Jane Day, Abraham A. Ungar, J. E. White, Paul Turner, Roger B. Nelsen and Bruce Reznick. Their work appears in journals such as Optimization methods & software, American Mathematical Monthly, College Mathematics Journal, International Journal of Mathematical Education in Science and Technology and PRIMUS.

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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