Yukai’s PhD research focuses on data-efficient machine learning for understanding structure-property relationships in protein-based materials. His project will investigate how statistical surrogate modelling and image analysis techniques can be used to extract low-dimensional representations from atomic force microscopy (AFM) images and relate them to macroscopic material properties. The research will also explore uncertainty quantification under data scarcity, with the aim of supporting principled experimental design and scientific discovery.

Project: Data-efficient machine learning for protein-based materials

✨ Favourite Activities

• 🏋️ Gym training
• 🌳 Walking in parks
• 🎲 Social deduction games, such as Avalon