Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop. - Catherine F. Higham (BMC Syst. Biol., 2009)

A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. - Catherine F. Higham; Dirk Husmeier (BMC Bioinform., 2013)

Controversy in mechanistic modelling with Gaussian processes. - Benn Macdonald; Catherine F. Higham; Dirk Husmeier (ICML, 2015)

Inference of Circadian Regulatory Pathways Based on Delay Differential Equations. - Catherine F. Higham; Dirk Husmeier (IWBBIO (2), 2015)

Neural network identification of people hidden from view with a single-pixel, single-photon detector. - Piergiorgio Caramazza; Alessandro Boccolini; Daniel Buschek; Matthias B. Hullin; Catherine F. Higham; Robert Henderson; Roderick Murray-Smith; Daniele Faccio (CoRR, 2017)

Deep Learning: An Introduction for Applied Mathematicians. - Catherine F. Higham; Desmond J. Higham (SIAM Rev., 2019)

Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability. - Clara Cohen; Catherine F. Higham; Syed Waqar Nabi (Frontiers Artif. Intell., 2020)

Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer. - Catherine F. Higham; Adrian Bedford (CoRR, 2021)

Core-periphery Partitioning and Quantum Annealing. - Catherine F. Higham; Desmond J. Higham; Francesco Tudisco (KDD, 2022)

Testing a QUBO Formulation of Core-periphery Partitioning on a Quantum Annealer. - Catherine F. Higham; Desmond J. Higham; Francesco Tudisco (CoRR, 2022)

Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians. - Catherine F. Higham; Desmond J. Higham; Peter Grindrod (CoRR, 2023)

Deceptive Diffusion: Generating Synthetic Adversarial Examples. - Lucas Beerens; Catherine F. Higham; Desmond J. Higham (SSVM (1), 2025)

Actively Inferring Optimal Measurement Sequences. - Catherine F. Higham; Paul Henderson; Roderick Murray-Smith (CoRR, 2025)