A fast, spectrally accurate homotopy based numerical method for solving nonlinear differential equations. - Andrew C. Cullen; Simon R. Clarke (J. Comput. Phys., 2019)

A novel numerical solver for nonlinear boundary value problems, with applications to the forced Gardner equation. - Andrew C. Cullen (2019)

Distributed Generative Adversarial Networks for Anomaly Detection. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie; Justin Kopacz (GameSec, 2020)

Privacy-Preserving Collaborative SDR Networks for Anomaly Detection. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie; Justin Kopacz (ICC, 2021)

Domain-Aware Multiagent Reinforcement Learning in Navigation. - Ifrah Saeed; Andrew C. Cullen; Sarah M. Erfani; Tansu Alpcan (IJCNN, 2021)

Wireless Network Simulation to Create Machine Learning Benchmark Data. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie; Justin Kopacz (GLOBECOM, 2022)

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. - Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein (NeurIPS, 2022)

Generative Adversarial Networks for anomaly detection on decentralised data. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie (Annu. Rev. Control., 2022)

Adversarial Decisions on Complex Dynamical Systems using Game Theory. - Andrew C. Cullen; Tansu Alpcan; Alexander C. Kalloniatis (CoRR, 2022)

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks. - Shijie Liu; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (AAAI, 2023)

An Adversarial Strategic Game for Machine Learning as a Service using System Features. - Guoxin Sun; Tansu Alpcan; Seyit Camtepe; Andrew C. Cullen; Benjamin I. P. Rubinstein (AAMAS, 2023)

Quantisation Effects in Adversarial Cyber-Physical Games. - Takuma Adams; Andrew C. Cullen; Tansu Alpcan (GameSec, 2023)

Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie (ICC, 2023)

Exploiting Certified Defences to Attack Randomised Smoothing. - Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2023)

Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks. - Marc Katzef; Andrew C. Cullen; Tansu Alpcan; Christopher Leckie; Justin Kopacz (CoRR, 2023)

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction. - Andrew C. Cullen; Benjamin I. P. Rubinstein; Sithamparanathan Kandeepan; Barry Flower; Philip H. W. Leong (Artif. Intell. Rev., 2023)

Reinforcement Learning for Joint Resource and Power Allocation in D2D Communications. - Ifrah Saeed; Andrew C. Cullen; Zainab R. Zaidi; Sarah M. Erfani; Tansu Alpcan (CDC, 2024)

Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples. - Andrew C. Cullen; Shijie Liu; Paul Montague; Sarah Monazam Erfani; Benjamin I. P. Rubinstein (ICML, 2024)

It's Simplex! Disaggregating Measures to Improve Certified Robustness. - Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein (SP, 2024)

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning. - Shijie Liu; Andrew Craig Cullen; Paul Montague; Sarah Monazam Erfani; Benjamin I. P. Rubinstein (ICLR, 2025)

Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning. - Shijie Liu; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2025)

Position: Certified Robustness Does Not (Yet) Imply Model Security. - Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2025)

Suboptimality of Constrained Action Adversarial Cyber-Physical Games. - Takuma A. Adams; Andrew C. Cullen; Tansu Alpcan (Dyn. Games Appl., 2025)

Game-Theoretic Analysis of Adversarial Decision Making in a Complex Socio-Physical System. - Andrew C. Cullen; Tansu Alpcan; Alexander C. Kalloniatis (Dyn. Games Appl., 2025)