Greg is Professor of Computer Vision and AI and Director of the Digital Environment Research Institute (DERI) at Queen Mary. His primary research interests include computer vision and deep learning, with applications to computational photography and medical image computing.
Prior to joining Queen Mary University of London in 2020, he was Chief Scientist in Computer Vision (EU) for Huawei Technologies R&D where he led a team of research scientists working in computational photography, studying the camera image signal processor (ISP) pipeline including denoising, demosaicing, automatic white balance, super-resolution, and colour enhancement for high quality photographs and video. Earlier industrial appointments include Medicsight, where he led a team of research scientists in detection of pre-cancerous lesions in the colon and lung, imaged with computed tomography; with the company's ColonCAD product receiving FDA clearance and CE marking. He also was an employee of Siemens, where he performed research in medical image computing and 3D shape modelling. He holds 36 granted patents and has roughly 200 publications.
He earned a PhD in Electrical Engineering from Georgia Institute of Technology in Atlanta, USA where his thesis focused on reconstruction of 3D shapes from 2D photographs. For six years he was an academic at City, University of London where he taught modules in computer vision, graphics, computer games technology, and programming in addition to leading research grants funded by the European Commission, EPSRC and Innovate UK. He was awarded a university-wide Research Student Supervision Award in 2017, and a Teaching in the Schools award for the School of Mathematics, Computer Science, and Engineering in 2016.
More details can be found at http://eecs.qmul.ac.uk/~gslabaugh/
- Joint Dense-Point Representation for Contour-Aware Graph Segmentation, Kit Bransby, Qianni Zhang, Gregory Slabaugh, Christos Bourantas, Medical Image Analysis and Computer-Aided Interventions (MICCAI), 2023.
- MOAB: Multi-modal Outer Arithmetic Block for Fusion of Histopathological Images and Genetic Data for Brain Tumor Grading, Omnia Alwazzan, Abbas Khan, Yiannis Patras, Gregory Slabaugh, International Symposium on Biomedical Imaging (ISBI), 2023.
- Improving Dynamic HDR Imaging with Fusion Transformer, Chen Rufeng, Bolun Zheng, Hua Zhang, Quan Chen, Chenggang Yan, Greg Slabaugh, Shanxin Yuan, AAAI Conference on Artificial Intelligence, 2023.
- Diagnosing and Preventing Instabilities in Recurrent Video Processing, Thomas Tanay, Aivar Sootla, Matteo Maggioni, Puneet K. Dokania, Philip Torr, Ales Leonardis, Gregory Slabaugh, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2022.
- FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging, Sibi Catley-Chandar, Thomas Tanay, Lucas Vandroux, Aleš Leonardis, Gregory Slabaugh, Eduardo Pérez-Pellitero, IEEE Transactions on Image Processing (T-IP), 2022.
- Learning Frequency Domain Priors for Image Demoireing, Bolun Zheng, Shanxin Yuan, Chenggang Yan, Xiang Tian, Jiyong Zhang, Yaoqi Sun, Lin Liu, Ales Leonardis, Greg Slabaugh, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2021.
- Continual Learning: A Comparative Study on How to Defy Forgetting in Classification Tasks, Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2021.
Currently Greg is first-supervising the following PhD students:
- (Knowledge base lead) Accelerated Knowledge Transfer to Innovate (AKT2I) with Keen AI, Innovate UK, 2023
- (Co-Investigator) An Ecosystem for Digital Twins in Healthcare, Horizon Europe, 2023-2025
- (Research Collaborator) Biomedical Research Centre, NIHR, 2022-2027
- (Knowledge base lead) Queen Mary University of London and Wise Plc Knowledge Transfer Partnership (KTP), 2022-2023
- (Co-Investigator) Collaborative Training Partnership in AI for Drug Discovery, led by Exscientia PLC, BBRSC, 2022-2028