Daniel L. Sussman

2.9k total citations
24 papers, 888 citations indexed

About

Daniel L. Sussman is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daniel L. Sussman has authored 24 papers receiving a total of 888 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Statistical and Nonlinear Physics and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daniel L. Sussman's work include Complex Network Analysis Techniques (9 papers), Advanced Graph Neural Networks (6 papers) and Bayesian Methods and Mixture Models (3 papers). Daniel L. Sussman is often cited by papers focused on Complex Network Analysis Techniques (9 papers), Advanced Graph Neural Networks (6 papers) and Bayesian Methods and Mixture Models (3 papers). Daniel L. Sussman collaborates with scholars based in United States, Germany and United Kingdom. Daniel L. Sussman's co-authors include Silvana B. Rosso, Anthony Wynshaw‐Boris, Patricia C. Salinas, Carey E. Priebe, Minh Tang, Donniell E. Fishkind, Vince Lyzinski, Avanti Athreya, Ronald M. Summers and Jianhua Yao and has published in prestigious journals such as Journal of the American Statistical Association, Nature Neuroscience and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Daniel L. Sussman

24 papers receiving 872 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel L. Sussman United States 12 391 233 180 164 111 24 888
Tom Michoel United Kingdom 21 939 2.4× 68 0.3× 180 1.0× 64 0.4× 182 1.6× 68 2.2k
Anmar Khadra Canada 21 234 0.6× 302 1.3× 120 0.7× 82 0.5× 240 2.2× 69 1.5k
Shahin Mohammadi United States 13 1.3k 3.3× 120 0.5× 279 1.6× 136 0.8× 94 0.8× 20 2.2k
David Choi United States 15 228 0.6× 155 0.7× 20 0.1× 141 0.9× 34 0.3× 28 875
László Négyessy Hungary 19 517 1.3× 239 1.0× 548 3.0× 106 0.6× 45 0.4× 46 1.4k
Gregorio Alanis‐Lobato Germany 16 1.0k 2.7× 331 1.4× 60 0.3× 226 1.4× 132 1.2× 33 1.4k
John Bogovic United States 18 265 0.7× 17 0.1× 133 0.7× 75 0.5× 48 0.4× 42 1.3k
Dawei Dong United States 23 717 1.8× 45 0.2× 212 1.2× 98 0.6× 25 0.2× 55 1.7k
Fabio Anselmi Italy 17 453 1.2× 42 0.2× 93 0.5× 203 1.2× 34 0.3× 35 1.1k
Geoffrey Fox Germany 14 301 0.8× 20 0.1× 236 1.3× 175 1.1× 13 0.1× 40 859

Countries citing papers authored by Daniel L. Sussman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel L. Sussman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel L. Sussman

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel L. Sussman. A scholar is included among the top collaborators of Daniel L. Sussman 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 Daniel L. Sussman. Daniel L. Sussman 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.
Sussman, Daniel L., et al.. (2022). Estimation of the Branching Factor in Noisy Networks. IEEE Transactions on Network Science and Engineering. 10(1). 565–577. 2 indexed citations
2.
Sussman, Daniel L., et al.. (2022). Multiplex graph matching matched filters. Applied Network Science. 7(1). 2 indexed citations
3.
Sussman, Daniel L., et al.. (2022). Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings. IEEE Transactions on Network Science and Engineering. 10(1). 346–359. 2 indexed citations
4.
Arroyo, Jesús, Daniel L. Sussman, Carey E. Priebe, & Vince Lyzinski. (2021). Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks. Journal of Computational and Graphical Statistics. 30(4). 1111–1123. 2 indexed citations
5.
Lyzinski, Vince & Daniel L. Sussman. (2020). Matchability of heterogeneous networks pairs [Image: see text]. PubMed Central. 4 indexed citations
6.
Ketcha, Michael D., Alexandra Badea, Evan Calabrese, et al.. (2018). Connectome smoothing via low-rank approximations. IEEE Transactions on Medical Imaging. 38(6). 1446–1456. 17 indexed citations
7.
Athreya, Avanti, Donniell E. Fishkind, Minh Tang, et al.. (2018). Statistical inference on random dot product graphs: a survey. Journal of Machine Learning Research. 18(226). 1–92. 47 indexed citations
8.
Tang, Minh, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, & Carey E. Priebe. (2017). A nonparametric two-sample hypothesis testing problem for random graphs. Bernoulli. 23(3). 33 indexed citations
9.
Priebe, Carey E., et al.. (2015). A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs. Sankhya A. 78(1). 1–18. 41 indexed citations
10.
Athreya, Avanti, Vince Lyzinski, David J. Marchette, et al.. (2013). A limit theorem for scaled eigenvectors of random dot product graphs. arXiv (Cornell University). 3 indexed citations
11.
Sussman, Daniel L., Minh Tang, & Carey E. Priebe. (2013). Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(1). 48–57. 46 indexed citations
12.
Tilton, James C., Douglas C. Comer, Carey E. Priebe, Daniel L. Sussman, & Li Chen. (2012). Refinement of a method for identifying probable archaeological sites from remotely sensed data. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8390. 83901K–83901K. 5 indexed citations
13.
Sussman, Daniel L., Minh Tang, Donniell E. Fishkind, & Carey E. Priebe. (2012). A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs. Journal of the American Statistical Association. 107(499). 1119–1128. 117 indexed citations
14.
Summers, Ronald M., Jiamin Liu, Daniel L. Sussman, et al.. (2012). Association Between Visceral Adiposity and Colorectal Polyps on CT Colonography. American Journal of Roentgenology. 199(1). 48–57. 21 indexed citations
15.
Sussman, Daniel L., Minh Tang, Donniell E. Fishkind, & Carey E. Priebe. (2011). A consistent dot product embedding for stochastic blockmodel graphs. arXiv (Cornell University). 5 indexed citations
16.
Yao, Jianhua, Daniel L. Sussman, & Ronald M. Summers. (2011). Fully automated adipose tissue measurement on abdominal CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7965. 79651Z–79651Z. 20 indexed citations
17.
Sussman, Daniel L., Jianhua Yao, & Ronald M. Summers. (2010). Automated measurement and segmentation of abdominal adipose tissue in MRI. Zenodo (CERN European Organization for Nuclear Research). 35. 936–939. 5 indexed citations
18.
Rosso, Silvana B., Daniel L. Sussman, Anthony Wynshaw‐Boris, & Patricia C. Salinas. (2004). Wnt signaling through Dishevelled, Rac and JNK regulates dendritic development. Nature Neuroscience. 8(1). 34–42. 396 indexed citations
19.
20.
Yan, Guochen, Fen Wang, Yoshitatsu Fukabori, et al.. (1992). Expression and transforming activity of a variant of the heparin-binding fibroblast growth factor receptor (flg) gene resulting from splicing of the alpha exon at an alternate 3′-acceptor site. Biochemical and Biophysical Research Communications. 183(2). 423–430. 39 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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