Morten Rieger Hannemose is Associate Professor in Visual Computing at DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark. He holds a PhD in differentiable formulations for inverse rendering and conducted research at Cornell Tech with Professor Serge Belongie. His work spans computer vision and deep learning, with particular expertise in generative models, diffusion-based image synthesis, 3D surface reconstruction, camera calibration, and pose estimation — increasingly combining images with other data modalities such as text and sensor input. He applies these methods to real-world, high-impact problems including medical image generation, skin lesion decision support, and retinal vessel segmentation, supervising several PhD students across healthcare and remote sensing applications. At Digital Tech Summit, he brings a computer vision perspective to multi-modal AI, exploring how combining visual data with other information sources enables richer, more robust, and more broadly applicable AI systems for industry.