Leonard J. Schulman

96 papers receiving 2.0k citations

Peers

Leonard J. Schulman
Comparison fields: 5 of 105
  • Artificial Intelligence 1.1k
  • Computational Theory and Mathematics 700
  • Computer Networks and Communications 504
  • Electrical and Electronic Engineering 351
  • Computer Vision and Pattern Recognition 234
Replace Robert Krauthgamer with:
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Leonid Khachiyan United States
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Leonard J. Schulman relative to Robert Krauthgamer Israel Robert Krauthgamer's profile →
Citations per field
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Citations per year

Countries citing papers authored by Leonard J. Schulman

Since Specialization
Citations

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

Fields of papers citing papers by Leonard J. Schulman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leonard J. Schulman. 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 Leonard J. Schulman. The network helps show where Leonard J. Schulman may publish in the future.

Co-authorship network of co-authors of Leonard J. Schulman

This figure shows the co-authorship network connecting the top 25 collaborators of Leonard J. Schulman. A scholar is included among the top collaborators of Leonard J. Schulman 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 Leonard J. Schulman. Leonard J. Schulman 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
#WorkIndexed citations
1 4
2 5
3 1
4
Stability of causal inference
0
5 14
6 15
7
Approximation algorithms for labeling hierarchical taxonomies
1
8
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
51
9
Contraction and Expansion of Convex Sets.
1
10 5
11 69
12
Deterministic Clustering with Data Nets
19
13 7
14 38
15 15
16
Clustering for Edge-Cost Minimization
39
17 2
18 2
19 104
20 2

About Leonard J. Schulman

Leonard J. Schulman is a scholar working on Computer Graphics and Computer-Aided Design, Computational Theory and Mathematics and Computational Mathematics, having authored 102 papers that have together received 2.2k indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (19 papers), Quantum Computing Algorithms and Architecture (14 papers) and Machine Learning and Algorithms (13 papers). The work is most often cited by research in Computational Theory and Mathematics (700 citations), Computer Graphics and Computer-Aided Design (133 citations) and Artificial Intelligence (1.1k citations). Leonard J. Schulman has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include William Evans, Rafail Ostrovsky, Yuval Rabani, Umesh Vazirani, Chaitanya Swamy, Michael Mitzenmacher, Moni Naor, Massimo Franceschetti, Jehoshua Bruck and Sanjoy Dasgupta. Their work appears in journals such as Physical Review Letters, IEEE Transactions on Information Theory and IEEE Transactions on Antennas and Propagation.

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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