Ph.D, Deep Learning in Cancer Imaging, The Institute of Cancer Research, University of London, United Kingdom

October 2019 - Present (Expected October 2023)

• Supervised and unsupervised medical image synthesis (e.g. pix2pix, cycle-GAN, probabilistic diffusion models)
• Volumetric pelvic tumor segmentation on magnetic resonance imaging (MRI) (e.g. U-Net, transformers, attention CNNs)
• Domain adaptation and large-scale transfer learning
• Automatic radiotherapy treatment planning
• Self-supervised neural network training (e.g. contrastive learning)
• Explainable AI