CHAIR research

AI and machine learning (ML) have tremendous potential for improving the healthcare system by standardizing care across health providers while personalizing it to individual patients.

This theme is about utilizing the power of AI as a tool for scientific research. AI can be applied to, and potentially speed up, discovery and utilization in a variety of research disciplines, such as microscopy, physics, biology, chemistry, and astronomy.

Interpretable AI is an emerging field, focused on developing AI systems that are transparent and understandable to humans.

Large language models, such as Chat-GPT, have demonstrated remarkable abilities to understand and generate human language. This theme explores their potential to contribute to mathematical reasoning and software creation, extending beyond traditional methods and potentially uncovering novel approaches and solutions.

This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.