Explore – webinars
It is a series of webinars hosted by the University of Edinburgh allowing companies and organisations to hear directly from our leading researchers.
The series features experts from different areas sharing insight into their expertise and giving examples of concrete applications.
The Explore webinars offer a unique opportunity for organisations across multiple sectors to learn about the recent research and capabilities in AI, data science, quantum computing, robotics and more.
October & December 2024 Explore webinars:
- 24 October 2.00-3.00pm – Explore AI Frontiers
- Innovations across AI and Semiconductors – Themis Prodromakis
- 11 December 2.00-3.00pm – Explore Data Science Frontiers
- What makes a good embedding? – Patrick Rubin-Delanchy
Are you looking to collaborate with our leading experts on AI, data science, quantum computing, robotics? Submit your Digital Frontiers challenge to AIMday Digital Frontiers on 26 February 2025.
Speaker presentations
Professor Themis Prodromakis holds the Regius Chair of Engineering at the University of Edinburgh and is Director of the Centre for Electronics Frontiers. His work focuses on developing energy-efficient AI hardware solutions through innovating novel semiconductor technologies and neuromorphic computing architectures. He leads an interdisciplinary team comprising 50 researchers with expertise across materials process development to electron devices and circuits and systems for applications in embedded systems and AI. He holds an RAEng Chair in Emerging Technologies and is Adjunct Professor at UTS Australia and Honorary Fellow at Imperial College London. He is Fellow of the Royal Society of Chemistry, the British Computer Society, the IET and the Institute of Physics. He is the Director of the UKRI APRIL AI Hub that is developing AI tools and capabilities for the electronics sector. In 2015, he established ArC Instruments Ltd that delivers high-performance testing infrastructure for automating characterisation of novel nanodevices in over 26 countries. His contributions in memristive technologies and applications have brought this emerging technology one step closer to the electronics industry for which he was recognised as a 2021 Blavatnik Award UK Honoree in Physical Sciences and Engineering.
Professor Patrick Rubin-Delanchy is the chair of Statistical Learning at the University of Edinburgh as of January 2024. His research interests include data exploration, embedding and machine learning with a particular focus on structure discovery using AI, for example, correlations, clusters, hierarchy, trends, or manifold structure; in complex data such as large relational databases, dynamic networks, or high-dimensional data (e.g. tables with many columns, text, images). Applications of this research are wide-ranging, and he has won funding (over £7M between government & industry) for applications in biosciences, healthcare, (cyber-)security, societal resilience, environmental protection and more.
Abstract of the talk:
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 talk I 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. I illustrate these points with 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.