Rate My Professor Greg Slabaugh

Professor Greg Slabaugh serves as Director of the Digital Environment Research Institute (DERI) and Professor of Computer Vision and AI in the School of Electronic Engineering and Computer Science at Queen Mary University of London since 2020. He also holds the position of Academic Turing Liaison for Queen Mary at The Alan Turing Institute since 2023. Slabaugh obtained his PhD in Electrical Engineering from the Georgia Institute of Technology in Atlanta, USA, with a doctoral thesis on the reconstruction of 3D shapes from 2D photographs. His research focuses on computer vision and deep learning, particularly applications in computational photography and medical image computing. He has authored over 250 peer-reviewed publications and holds 49 granted patent families.

Before joining Queen Mary, Slabaugh was Chief Scientist in Computer Vision for Huawei Technologies R&D in Europe, leading a team that advanced computational photography through work on the camera image signal processor pipeline, encompassing denoising, demosaicing, automatic white balance, super-resolution, and colour enhancement. At Medicsight, he directed a research team developing algorithms to detect pre-cancerous lesions in the colon and lungs via computed tomography; their ColonCAD product achieved FDA clearance and CE marking. Earlier, he conducted research in medical image computing and 3D shape modelling at Siemens Corporate Research. Slabaugh spent six years as an academic at City, University of London, teaching modules in computer vision, graphics, computer games technology, and programming, while leading grants from the European Commission, EPSRC, and Innovate UK. He has received a university-wide Research Student Supervision Award in 2017 and a Teaching in the Schools award in 2016 from the School of Mathematics, Computer Science, and Engineering. Slabaugh regularly contributes to technical program committees for conferences including CVPR, NeurIPS, and AAAI. Selected recent publications include BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology (CVPR, 2025), XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification (MICCAI, 2025), and RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement (ECCV, 2024).