AI in Science Fellowship Q+A Session
16/06/2026
16:00 - 17:00This informal Q+A is for anyone interested in finding out more about the AI in Science Postdoctoral Fellowships before applying to the scheme.
This informal Q+A is for anyone interested in finding out more about the AI in Science Postdoctoral Fellowships before applying to the scheme.
From running mobile clinics with semi-nomadic polygamous tribes to running McKinsey’s global Organization practice, Dr Mary Meaney Haynes will share some perspectives on a career in consulting, serving on Boards of Fortune500’s and Imperial College Council and her recent work supporting Ukrainian refugees.
Graph Neural Networks (GNNs) are the most widely used techniques for handling unstructured data and have demonstrated success in various applications, such as drug discovery and recommendation systems.
In this talk, Hamed will introduce the NetSys group’s research. They work on a broad range of research topics including User-Centred Systems, IoT, Applied Machine Learning, Privacy, and Human-Data Interaction.
Message passing (MP) stands as a cornerstone in Geometric Deep Learning, driving the success of Graph Neural Networks (GNNs) in analysing both graphs and point clouds, and leading to empirical achievements in many scientific domains. Despite its widespread application, the theoretical underpinnings of MP’s successes and limitations remain underexplored.
This talk will primarily serve as an overview of the work being carried out in I-X’s Ecosystem Sensing group.
Deep learning has shown great potential in improving and accelerating the entire medical imaging workflow, from image acquisition to interpretation. This talk will focus on the recent advances of deep learning in medical imaging, from the reconstruction of accelerated signals to automatic quantification of clinically useful information.
