Explore Data Science Frontiers: What makes a good embedding?

Embeddings are continuous vector representations of entities, such as words or nodes, perhaps most widely known for their role in modern AI systems such as large language models. In this webinar from December 2024, Professor Patrick Rubin-Delanchy, Chair of Statistical Learning at the University of Edinburgh, will consider a different goal, which is facilitating statistical analysis, and the creation of knowledge.


An embedding is an instrument which allows us to observe complex, unstructured, or otherwise intractable data, in a way that we can use. In embeddings, classical (e.g. Gaussian) statistical models are tenable; concepts like similarity, or trend, have a ‘shape’; abstract notions such as political opinion, the health of a patient, the function of a cell, can be made geometric and measurable; and we can uncover truths that could have seemed completely absent from the unprocessed data. He will illustrate these points with a new theory connecting statistical models, embeddings and the manifold hypothesis, and with motivating problems in science, security, and recent work with Southmead Hospital at Bristol.