CO2 geological storage plays an essential role in global decarbonization and the energy transition. Predicting the transport of CO2 in subsurface formations requires the numerical simulation of multiphase flow through porous media.
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.
