Daniel Dunlavy

2.4k citations
34 papers · 1.1k indexed · 1 hit paper · h-index 14

Impact in

Papers in

Daniel Dunlavy

32 papers receiving 1.1k citations

Hit Papers

Scalable tensor factorizations for incomplete data 2010 · 420 citations
4202010202620152020100200300400

Peers

Daniel Dunlavy
Comparison fields: 5 of 103
  • Computational Mathematics 555
  • Signal Processing 150
  • Statistical and Nonlinear Physics 170
  • Computational Mechanics 263
  • Artificial Intelligence 339
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Citations per field
00.5×4.9×
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Citations per year

Countries citing papers authored by Daniel Dunlavy

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Dunlavy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Daniel Dunlavy, 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 Daniel Dunlavy Line = papers co-authored together Daniel Dunlavy links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20230
3 20221
4 20210
5 20211
6 20208
7 201913
8 201624
9 20125
10
TopicView: Understanding Document Relationships Using Latent Dirichlet Allocation Models.
20113
11 201122
12 201094
13
Mathematical Challenges in Cybersecurity
200914
14
Relationships Between Accuracy and Diversity in Heterogeneous Ensemble Classiers
20093
15
SEMISUPERVISED NAMED ENTITY RECOGNITION
20092
16 200760
17 200521
18
From TREC to DUC to TREC Again.
20036
19
Performance of a Three-Stage System for Multi-Document Summarization
200311
20 200314

About Daniel Dunlavy

Daniel Dunlavy is a scholar working on Computational Mathematics, Hardware and Architecture, Artificial Intelligence, Numerical Analysis and Computer Networks and Communications, having authored 34 papers that have together received 1.1k indexed citations. Recurring topics across this work include Tensor decomposition and applications (12 papers), Topic Modeling (5 papers), Parallel Computing and Optimization Techniques (5 papers), Natural Language Processing Techniques (5 papers), Advanced Text Analysis Techniques (4 papers), Computational Physics and Python Applications (4 papers), Algorithms and Data Compression (4 papers) and Data Visualization and Analytics (3 papers). The work is most often cited by research in Computational Mathematics (555 citations), Signal Processing (150 citations), Statistical and Nonlinear Physics (170 citations), Computational Mechanics (263 citations) and Artificial Intelligence (339 citations). Daniel Dunlavy has collaborated with scholars based in United States, Denmark and Türkiye. Frequent co-authors include Tamara G. Kolda, Evrim Acar, Morten Mørup, Michael Eldred, Dianne P. O’Leary, John M. Conroy, Judith D. Schlesinger, Patricia J. Crossno, Michael M. Wolf and Andrew T. Wilson. Their work appears in journals such as Information and Inference A Journal of the IMA, Optimization methods & software, Information Processing & Management, Journal of Computational Biology and Journal of Chemometrics.

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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