Liam Fowl

516 total citations
9 papers, 16 citations indexed

About

Liam Fowl is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Liam Fowl has authored 9 papers receiving a total of 16 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 1 paper in Molecular Biology and 1 paper in Information Systems. Recurrent topics in Liam Fowl's work include Adversarial Robustness in Machine Learning (6 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Bacillus and Francisella bacterial research (1 paper). Liam Fowl is often cited by papers focused on Adversarial Robustness in Machine Learning (6 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Bacillus and Francisella bacterial research (1 paper). Liam Fowl collaborates with scholars based in United States. Liam Fowl's co-authors include Tom Goldstein, Micah Goldblum, Jonas Geiping, Gavin Taylor, Wei Huang, John P. Dickerson, Quan Wang, David Jacobs, Ignacio López Moreno and Christoph Studer and has published in prestigious journals such as arXiv (Cornell University), Neural Information Processing Systems and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

In The Last Decade

Liam Fowl

8 papers receiving 16 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Liam Fowl United States	3	14	4	3	1	1	9	16
Huaxiong Wang Singapore	3	14 1.0×	3 0.8×	2 0.7×			3	16
Cem Anil United Kingdom	1	10 0.7×	4 1.0×	2 0.7×	1 1.0×		2	12
Siu-Ming Yiu Hong Kong	2	15 1.1×	2 0.5×	3 1.0×	1 1.0×		2	16
S. Shi China	2	13 0.9×	7 1.8×	2 0.7×	1 1.0×		2	35
Igor Gitman United States	2	11 0.8×	7 1.8×	2 0.7×			3	14
Abdelwahab Heba Saudi Arabia	3	9 0.6×	3 0.8×	2 0.7×			3	12
Lean Wang China	2	7 0.5×	6 1.5×	3 1.0×		1 1.0×	3	13
Karsten Luebke Germany	3	10 0.7×	5 1.3×	2 0.7×	1 1.0×		9	15
Riham Mansour Egypt	3	9 0.6×	2 0.5×	3 1.0×			5	15
Iroro Orife United States	3	12 0.9×	7 1.8×	6 2.0×			6	19

Countries citing papers authored by Liam Fowl

Since Specialization

Citations

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

Fields of papers citing papers by Liam Fowl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liam Fowl

This figure shows the co-authorship network connecting the top 25 collaborators of Liam Fowl. A scholar is included among the top collaborators of Liam Fowl 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 Liam Fowl. Liam Fowl 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:

9 of 9 papers shown

1.

Fowl, Liam, et al.. (2025). 3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering. 1–5.

2.

Moreno, Ignacio López, et al.. (2023). Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting. Biblos-e Archivo (Universidad Autónoma de Madrid). 1–5. 2 indexed citations

3.

Fowl, Liam, et al.. (2022). Poisons that are learned faster are more effective. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 197–204. 2 indexed citations

4.

Fowl, Liam, et al.. (2021). Protecting Proprietary Data: Poisoning for Secure Dataset Release. 1 indexed citations

5.

Huang, Wei, Jonas Geiping, Liam Fowl, Gavin Taylor, & Tom Goldstein. (2020). MetaPoison: Practical General-purpose Clean-label Data Poisoning. arXiv (Cornell University). 33. 12080–12091. 3 indexed citations

6.

Goldblum, Micah, Liam Fowl, & Tom Goldstein. (2020). Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. Neural Information Processing Systems. 33. 17886–17895. 2 indexed citations

7.

Fowl, Liam, et al.. (2020). Headless Horseman: Adversarial Attacks on Transfer Learning Models. arXiv (Cornell University). 3087–3091. 2 indexed citations

8.

Goldblum, Micah, Liam Fowl, & Tom Goldstein. (2019). Robust Few-Shot Learning with Adversarially Queried Meta-Learners. arXiv (Cornell University). 3 indexed citations

9.

Huang, Wei, et al.. (2019). Strong Baseline Defenses Against Clean-Label Poisoning Attacks. 1 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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