November Seminar Series
December 5, 2023
We were delighted to welcome Professor Paulo Missier who hosted last seminar series of 2023. The past few years have seen the emergence of what the AI community calls “Data-centric AI”, namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started exploring the connection between data and models in depth, along with startups that offer “data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from” than others.
In this “position talk”, Paulo suggested that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. He focused in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
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We are looking forward to welcoming Professor Honghan Wu, Professor of Health Informatics and AI at the University of Glasgow, who will deliver his talk “Large language model and Radiology: how to facilitate human and AI collaboration? " as part of our Seminar Series. Abstract: In this upcoming talk, Professor Honghan Wu explores the essential shift from viewing AI as a potential replacement for radiologists to recognizing it as a critical collaborative partner. Moving beyond basic tasks like detection and triage, the presentation highlights how AI can address practical clinical "pain points," such as reducing automated protocoling time by up to 60% and decreasing the time spent communicating with providers and patients by 30%. Professor Wu will present recent research on using knowledge-retrieval and Large Language Models for clinical report error correction and generation. The session concludes with an examination of the real-world deployment lifecycle, discussing the challenges of monitoring the over 700 FDA-cleared radiology AI devices currently in practice Seminar Series Event : “Large language model and Radiology: how to facilitate human and AI collaboration?" Date and Time: Thursday 25 June 2026, 15:00 – 16.00 hrs (BST) Location: Large Committee Room, Hodgkin Building, Guy's Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.
We are pleased to welcome Simon Ellershaw, PhD Candidate at University College London (UCL) as part of the UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, who will deliver his talk “Developing Healthcare LLMs: From the NHS to Silicon Valley " as part of our Seminar Series. Abstract: This talk links my PhD and my Silicon Valley internship through one theme: what it really takes to build and deploy LLMs in healthcare. I will introduce Foresight England (Foresight E), a national-scale generative foundation model trained from scratch on 54.9 million de-identified longitudinal NHS EHRs to model patient timelines and enable zero-shot prediction across around 40,000 coded medical events. As NHS England has paused data access pending review, I will focus on the core methodology and lessons learned. I will then switch to my Parexel internship in San Francisco, where I worked in the company’s AI lab on production-focused applications, including pharmacovigilance and protocol de-risking. I will explain how I ended up there, what I worked on, and what I learned, with a candid view of what day-to-day life and work in the Bay Area actually looks like. I will also reflect on how the recent generative AI boom has reshaped the problems teams like ours choose to tackle and the way this work gets built, evaluated, and shipped. Seminar Series Event : “Developing Healthcare LLMs: From the NHS to Silicon Valley" Date and Time: Wednesday 27 May 2026, 15:00 – 16.00 hrs (BST) Location: Judy Dunn, SGDP Building, Denmark Hill Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Simon Ellershaw is a PhD Candidate at University College London (UCL) as part of the UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, supervised by Prof Richard Dobson and Dr Anoop Shah. His research spans LLM-based generation of hospital discharge summaries, national-scale pre-training of generative models on 57 million electronic health records, and post-training using real-world patient outcomes as verifiable reinforcement-learning rewards. Alongside his PhD, he interned at Parexel AI Labs and now works part-time as an NLP Engineer, developing and deploying production LLM/NLP systems, including applications in pharmacovigilance and quality assurance.
