Professor Greg Slabaugh

Profile

Greg is Professor of Computer Vision and AI and director of the newly formed Digital Environment Research Institute (DERI) at Queen Mary. His primary research interests include computer vision, deep learning, computational photography, 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 companys 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 over 150 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.

Prof Slabaugh is also the Alan Turing Institute's Turing (Academic) Liaison on behalf of Queen Mary, as part of the University's role in the Turing University Network.

Research

Publications

• Drummond D, Adejumo I, Hansen K et al. (2024). Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges. nameOfConference

  
  

  DOI: 10.1183/20734735.0189-2023

  
  

  QMRO: qmroHref
• Shyam-Sundar V, Slabaugh G, Mohiddin SA et al. (2024). Clinical features, myocardial injury and systolic impairment in acute myocarditis.. nameOfConference

  
  

  DOI: 10.1136/openhrt-2024-002901

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/102496
• Bevis L, Misghina S, Rauseo E et al. (2024). Development of a User-Friendly Pipeline for Constructing Atrial Models at Scale: Importance of the End-User for Clinical Uptake. Computing in Cardiology 2024 (CinC24)

  
  

  DOI: 10.22489/CinC.2024.014

  
  

  QMRO: qmroHref
• Catley-Chandar S, Shaw R, Slabaugh G et al. (2025). RoGUENeRF: A Robust Geometry-Consistent Universal Enhancer for NeRF. European Conference on Computer Vision (2024)

  
  

  DOI: 10.1007/978-3-031-73254-6_4

  
  

  QMRO: qmroHref
• Gaintseva T, Benning M, Slabaugh G (2025). RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement. nameOfConference

  
  

  DOI: 10.1007/978-3-031-72986-7_24

  
  

  QMRO: qmroHref
• Jaffery OA, Zolotarev A, Barera CE et al. (2024). An in-silico comparison of anatomical and substrate based AF ablation using electro and optical flow mapping. nameOfConference

  
  

  DOI: 10.1093/eurheartj/ehae666.579

  
  

  QMRO: qmroHref
• Cheng F, Rauseo E, Misghina S et al. (2024). Biventricular modelling for investigating ventricular arrhythmias in silico. ESC 2024

  
  

  DOI: 10.1093/eurheartj/ehae666.3501

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101363
• Misghina S, Rauseo E, Barrera CE et al. (2024). In-silico insights into personalised therapy selection of anti-arrhythmic drugs and ablation using patient-specific biatrial models for atrial fibrillation. ESC 2024

  
  

  DOI: 10.1093/eurheartj/ehae666.3504

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101360
• Shyam-Sundar V, Nicholls H, Chadalavada S et al. (2024). The impact of multimorbidity on cardiac remodelling in the UK Biobank. ESC 2024

  
  

  DOI: 10.1093/eurheartj/ehae666.248

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101358
• Bransby KM, Bajaj R, Ramasamy A et al. (2024). POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation. nameOfConference

  
  

  DOI: 10.1016/j.compbiomed.2024.109162

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99711

Grants

• (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