Brooks Paige

Brooks Paige serves as an Associate Professor in Machine Learning at University College London's Department of Computer Science, where he leads research at the intersection of artificial intelligence, computational biology, and environmental science. His work bridges theoretical machine learning with high-impact applications in drug discovery, genomics, and climate modeling.

His research portfolio spans:

• Machine Learning (core methodology development)
• Artificial Intelligence (generative models and deep learning)
• Information Systems (data-intensive applications)
• Cognitive and Computational Psychology (human-AI interaction aspects)

Analysis of his 56 publications (2021-2025) reveals a dominant focus on generative modeling for molecular design, particularly protein-ligand binding prediction and antibody-epitope analysis. His methodological innovations include Gibbs sampling variants, Gaussian processes on non-Euclidean domains, and active learning frameworks, applied across biomedical and environmental domains including Arctic sea ice forecasting and urban analytics.

No scientific awards are documented in available sources. Similarly, student advisement records, research grant details, laboratory facilities, and collaborative team structures remain unspecified in the current dataset.