Prof Greg Slabaugh

Research

Computer vision, Medical image computing, Computational photography, Generative AI, Multimodal AI, Graph neural networks

Interests

I am interested in computer vision using deep learning techniques. This includes learning paradigms, neural network architectures, and applications across diverse topics. Currently my work explores image, video, surface, and graph representations of data, upon which deep neural networks operate, including convolutional, transformer, and state-space models in unimodal and multi-modal settings. Applications include healthcare, digital twins, drug discovery, computational photography, materials discovery, safety, vision-language models, video synthesis using generative AI.

Grants

Research Group

PhD Students

• Iqra Ali
  Longitudinal Modeling With Large Language Models and Applications in Mental Health
• Marco Anselmi
  Machine Learning Guided Discovery of Catalysts For Co2 Conversion
• Fuyu Cheng
  Investigating Ventricular Arrhythmia Disease Progression Through Uk Biobank Data Digital Twins.
• Zixu Cheng
  Vision
• William Dee
  Machine Learning Methods For Cell Painting in Drug Discovery
• Yilan Dong
  MMV
• Tatiana Gaintseva
  Semantic Control of Denoising Diffusion Probabilistic Models
• Ying He
  Attention-Guided Contextual Speech Mri Segmentation
• Manh Thang Hoang
  Development of a Multimodal Machine Learning Model For Heart Failure Classification and Prognosis Using Diverse Data Sources
• Ovais Ahmed Jaffery
  A Cohort of Virtual Human Atria With Personalised Electrophysiology For in-Silico Prediction of Ablation and Pharmacology Therapy Outcome
• Cian Kennedy
  Cog Sci
• Zimpi Komo
  Mirnas Sequence Embedding For Pathway Detection and Selectivity
• Nasim Mohamed Ismail
  MMV
• Byron Morris
  Development of Generalisable Multi-Modal Machine Learning Models For Digital Pathology
• Martina Occhetta
  Target Identification From Multi-Omics Data Using Systems Biology and Machine-Learning Approaches
• Sotirios Papadopoulos
  MMV
• Zara Saqlan
  A Machine Learning Model to Predict Preterm Birth
• Damith Senadeera
  MMV
• Henry Senior
  Exploring Structured Visual Representations For Image Captioning
• Qing Wang
  Multi-Modal Learning For Music Understanding
• Jiahao Yang
  MMV
• Tingting Yang
  Trustable Public Transit Through Integrated Multimodal Transit Networks and Trip Planning
• Xiaohang Yang
  MMV
• Miao Zhao
  Generative Modelling For Scalable and Representative Cardiac in Silico Trial Cohorts

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