Alessandro Sbrizzi is an assistant professor at the Imaging division of the UMC Utrecht. He graduated in Scientific Computing and obtained his PhD in 2013 with a thesis focused on modelling and numerical optimization of MRI acquisition & reconstruction processes. He is a recipient of a NWO-VENI grant 2016 and a NWO Demonstrator grant 2018.

Jelmer Wolterink is a postdoctoral researcher in the Quantitative Medical Image Analysis group at the Image Sciences Institute of the University Medical Center Utrecht. He obtained his PhD in 2017 with a thesis entitled Machine learning based analysis of cardiovascular images. His main research interests are the development and evaluation of machine learning methods for cardiac CT and MR images, in particular using deep neural networks. His current research focus is on generative models for synthesis of cardiovascular images.

Mark Savenije is a scientific programmer having a somewhat nomadic career moving accross several dutch institutes and universities. Funny enough, for the first time he actually works on topics he should be somewhat knowledgeable about as he graduated in the early 90's on the development of a self adaptive growing neural network (called the 'Muppet algorithm') which unfortunately did not survive a computer crash and is waiting to be rediscovered. In the mean time he investigates where deep learning techniques can be employed in the RT workflow (organ/CTV segmentation, synthetic CT's, CBCT correction).

Matteo Maspero is a physicist (freshly) enrolled as postdoctoral researcher at the Radiotherapy Department of UMC Utrecht. His main focuses are adaptive radiotherapy and MR-only RT. He uses deep learning trying to address image synthesis problem and dreaming (or having nightmares) about how to automatically QA the results within a clinical framework. Matteo is known for hyper-coloured presentations, let's see if he will keep up with the expectations... ORCID: https://orcid.org/0000-0003-0347-3375

After obtaining her PhD on prostate brachytherapy dose optimization (at the AMC), Anna Dinkla worked as a postdoctoral researcher at the department of Radiotherapy of the UMC in Utrecht on MR-only treatment planning for brain and head&neck radiotherapy. In collaboration with the Imaging Sciences Institute (ISI) she studied the use of synthetic CT for radiotherapy, generated with deep (convolutional) neural networks. She recently started as a medical physicist in training at the RT department in Amsterdam (VUMC).

Bjorn Stemkens is a postdoctoral researcher at the department of radiotherapy of the University Medical Center Utrecht and CEO of start-up company MR Code. He obtained his PhD in 2017 where he focussed on the development of MRI methods for MRI-guided radiotherapy. He is exploring deep learning applications for MRI-guided radiotherapy with a specific focus on accelerated MR image reconstruction, automation, and integration into the radiotherapy workflow.

Charis Kontaxis is a Clinical Computer Scientist at the Radiotherapy Department of University Medical Center Utrecht. His PhD was focused on online treatment plan adaptation for MRI-guided radiotherapy. He is now exploring deep learning applications in the treatment planning process and working towards clinical implementation of adaptive radiotherapy workflows.

Nico van den Berg is an MRI-physicist working as an Associate Professor at the Imaging Division in the department of Radiotherapy. He heads the newly formed group "Computational Imaging" that focuses on the development and application of new MR image acquisition, reconstruction and processing techniques for radiotherapy and diagnostic applications. In this context, Deep learning applications for image manipulations and information extraction became in the last years a key technology in the group's research.