Tanya Berger‐Wolf

4.8k total citations · 1 hit paper
103 papers, 2.6k citations indexed

About

Tanya Berger‐Wolf is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Molecular Biology. According to data from OpenAlex, Tanya Berger‐Wolf has authored 103 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 25 papers in Statistical and Nonlinear Physics and 19 papers in Molecular Biology. Recurrent topics in Tanya Berger‐Wolf's work include Complex Network Analysis Techniques (24 papers), Opinion Dynamics and Social Influence (15 papers) and Data Visualization and Analytics (10 papers). Tanya Berger‐Wolf is often cited by papers focused on Complex Network Analysis Techniques (24 papers), Opinion Dynamics and Social Influence (15 papers) and Data Visualization and Analytics (10 papers). Tanya Berger‐Wolf collaborates with scholars based in United States, Germany and Australia. Tanya Berger‐Wolf's co-authors include Chayant Tantipathananandh, Arun S. Maiya, Mayank Lahiri, Jared Saia, David Kempe, Daniel I. Rubenstein, Charles V. Stewart, Margaret C. Crofoot, Iain D. Couzin and Siva R. Sundaresan and has published in prestigious journals such as Nature Communications, Bioinformatics and PLoS ONE.

In The Last Decade

Tanya Berger‐Wolf

100 papers receiving 2.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Perspectives in machine learning for wildlife conservation

2022 348 citations Devis Tuia, Benjamin Kellenberger et al. Nature Communications profile →

Peers

Tanya Berger‐Wolf

SNP

AI

CVPR

ECOLO

CNC

Francisco A. Rodrigues Brazil

Bartolo Luque Spain

Marco Saerens Belgium

Satoru Kawai Japan

Mikko Kivelä Finland

Oliver Boisseau United Kingdom

Sergi Valverde Spain

Gary L. Miller United States

Elisabeth Slooten New Zealand

Chao Gao China

Francisco A. Rodrigues Brazil

Tanya Berger‐Wolf

2.2k ×2.7

SNP

587 ×1.1

AI

307 ×0.7

CVPR

87 ×0.2

ECOLO

1.2k ×2.9

CNC

Citations per year, relative to Tanya Berger‐Wolf Tanya Berger‐Wolf (= 1×) peers Francisco A. Rodrigues

Countries citing papers authored by Tanya Berger‐Wolf

Since Specialization

Citations

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

Fields of papers citing papers by Tanya Berger‐Wolf

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tanya Berger‐Wolf. 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 Tanya Berger‐Wolf. The network helps show where Tanya Berger‐Wolf may publish in the future.

Co-authorship network of co-authors of Tanya Berger‐Wolf

This figure shows the co-authorship network connecting the top 25 collaborators of Tanya Berger‐Wolf. A scholar is included among the top collaborators of Tanya Berger‐Wolf 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 Tanya Berger‐Wolf. Tanya Berger‐Wolf is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Stewart, Christopher, et al.. (2025). Studying collective animal behaviour with drones and computer vision. Methods in Ecology and Evolution. 16(10). 2229–2259. 1 indexed citations

2.

Zhang, Ziheng, et al.. (2025). Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation. 9611–9620. 2 indexed citations

3.

Pollock, Laura J., Justin Kitzes, Sara Beery, et al.. (2025). Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge. Edinburgh Research Explorer. 1(3). 166–182. 9 indexed citations

4.

Uyeda, Josef C., Meghan A. Balk, Wasila Dahdul, et al.. (2023). Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks. VTechWorks (Virginia Tech). 3966–3978. 3 indexed citations

5.

Gero, Shane, Mark A. Fisher, Gianna Minton, et al.. (2022). Flukebook: an open-source AI platform for cetacean photo identification. Mammalian Biology. 102(3). 1005–1023. 35 indexed citations

6.

Harel, Roi, Tanya Berger‐Wolf, Grace H. Davis, et al.. (2022). Life in 2.5D: Animal Movement in the Trees. Frontiers in Ecology and Evolution. 10. 7 indexed citations

7.

Tuia, Devis, Benjamin Kellenberger, Sara Beery, et al.. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications. 13(1). 792–792. 348 indexed citations breakdown →

8.

Llano, Daniel A., et al.. (2021). A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse. Network Neuroscience. 5(2). 569–590. 3 indexed citations

9.

Stewart, Charles V., et al.. (2017). Animal population censusing at scale with citizen science and photographic identification. National Conference on Artificial Intelligence. 37–44. 17 indexed citations

10.

Forbes, Angus G., et al.. (2016). SwordPlots: Exploring neuron behavior within dynamic communities of brain networks. 2016(16). 1–13. 2 indexed citations

11.

Li, Jia, et al.. (2015). Social information improves location prediction in the wild. National Conference on Artificial Intelligence. 25–32. 8 indexed citations

12.

Stewart, Charles V., et al.. (2013). HotSpotter — Patterned species instance recognition. ArXiv.org. 230–237. 104 indexed citations

13.

Saeed, Fahad, et al.. (2011). A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes. Journal of Parallel and Distributed Computing. 72(1). 83–93. 9 indexed citations

14.

Berger‐Wolf, Tanya, et al.. (2011). Working for Influence: Effect of Network Density and Modularity on Diffusion in Networks. 933–940. 12 indexed citations

15.

Crofoot, Margaret C., Daniel I. Rubenstein, Arun S. Maiya, & Tanya Berger‐Wolf. (2011). Aggression, grooming and group‐level cooperation in white‐faced capuchins (Cebus capucinus): insights from social networks. American Journal of Primatology. 73(8). 821–833. 36 indexed citations

16.

Ashley, Mary V., Tanya Berger‐Wolf, Piotr Berman, et al.. (2009). On approximating four covering and packing problems. Journal of Computer and System Sciences. 75(5). 287–302. 6 indexed citations

17.

Berger‐Wolf, Tanya, et al.. (2008). Consensus methods for reconstruction of sibling relationships from genetic data. National Conference on Artificial Intelligence. 97–102. 2 indexed citations

18.

Berger‐Wolf, Tanya, Cristopher Moore, & Jared Saia. (2006). A computational approach to animal breeding. Journal of Theoretical Biology. 244(3). 433–439. 3 indexed citations

19.

Berger‐Wolf, Tanya, William E. Hart, & Jared Saia. (2005). Discrete sensor placement problems in distribution networks. Mathematical and Computer Modelling. 42(13). 1385–1396. 48 indexed citations

20.

Berger‐Wolf, Tanya & Edward M. Reingold. (1999). Optimal multichannel communication under failure. Symposium on Discrete Algorithms. 858–859. 2 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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