Machine Learning for heterogeneous and multi-modal data

Contemporary digital world collects multiple forms of unstructured or semi-structured data that are inter-linked. Information is often represented and recorded in diverse and disparate formats, such as linguistic, images, videos, sketches, measurements, etc, possibly with missing data in some modalities. The proposed PhD research will investigate new approaches to machine learning from such complex data.