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

Liam Fowl
Comparison fields: 5 of 9
  • Artificial Intelligence 14
  • Computer Vision and Pattern Recognition 4
  • Signal Processing 3
  • Molecular Biology 1
  • Cognitive Neuroscience 1
Huaxiong Wang Singapore
Cem Anil United Kingdom
Siu-Ming Yiu Hong Kong
S. Shi China
Igor Gitman United States
Abdelwahab Heba Saudi Arabia
Lean Wang China
Karsten Luebke Germany
Riham Mansour Egypt
Iroro Orife United States
Huaxiong Wang Singapore View profile →
Citations per field, relative to Liam Fowl
Liam Fowl · 1×
Citations per year, relative to Liam Fowl
Liam Fowl · 1×

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

9 of 9 papers shown
# Work Indexed citations
1
3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering
2025 ·(unknown), Liam Fowl, Ramani Duraiswami
0
2
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting
2023 ·Biblos-e Archivo (Universidad Autónoma de Madrid) ·(unknown), (unknown), Ignacio López Moreno, (unknown), Liam Fowl, Quan Wang
2
3
Poisons that are learned faster are more effective
2022 ·2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ·(unknown), (unknown), Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein
2
4
Protecting Proprietary Data: Poisoning for Secure Dataset Release
2021 ·Liam Fowl, (unknown), Micah Goldblum, Jonas Geiping, (unknown), (unknown), Tom Goldstein
1
5
MetaPoison: Practical General-purpose Clean-label Data Poisoning
2020 ·arXiv (Cornell University) ·Wei Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
3
6
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
2020 ·Neural Information Processing Systems ·Micah Goldblum, Liam Fowl, Tom Goldstein
2
7
Headless Horseman: Adversarial Attacks on Transfer Learning Models
2020 ·arXiv (Cornell University) ·(unknown), (unknown), Liam Fowl, Tom Goldstein, Avi Schwarzschild, (unknown), Christoph Studer, Chen Zhu
2
8
Robust Few-Shot Learning with Adversarially Queried Meta-Learners
2019 ·arXiv (Cornell University) ·Micah Goldblum, Liam Fowl, Tom Goldstein
3
9
Strong Baseline Defenses Against Clean-Label Poisoning Attacks
2019 ·(unknown), Wei Huang, Liam Fowl, (unknown), Soheil Feizi, Tom Goldstein, John P. Dickerson
1

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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2026