OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
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For our first Machine Learning workshop we will be joined by Dr Lei Clifton, Programme Director of the MSc in Applied Digital Health, Primary Care Department, Dr Joshua Fieggen, DPhil candidate, CHI Lab, Department of Engineering Science and Greg Simond, DPhil student, NDPH.
Title: ML Workshop: Combining Machine Learning (ML) with Medical Statistics – A Worked Example
When: Thursday 26 February
Time: 11:00 – 12:00
Venue: OxPop Seminar room 0
In person only
Overview: As larger biomedical datasets emerge, it becomes increasingly challenging to identify potentially relevant features using only conventional approaches. In this workshop we will demonstrate how one can combine machine learning (ML) with classical statistical models for disease predictions, using worked examples on the UK Biobank.
Who it’s for: Any researcher curious about combining AI and statistics. No coding required for this session. Depending on the demand, we can deliver a hands-on coding session in the future, showing how we have implemented this approach in our published papers.
Bios:
Lei Clifton: Programme Director of the MSc in Applied Digital Health, Primary Care Department. Lei has 20+ years of experience at the intersection of medical statistics and AI. As Programme Director of the MSc in Applied Digital Health, she specialises in foundation models and large language models for healthcare, bringing expertise from engineering, machine learning, and medical statistics.
Joshua Fieggen: DPhil candidate, Computational Health Informatics (CHI) Lab. Josh is a medical doctor, and DPhil candidate from the CHI Lab in the Engineering Department. He has an MPH in Epidemiology and Biostatistics and his DPhil has focused on applications of ML and generative deep learning to the plasma proteomics data in UK Biobank.
Gregory Simond: MD-DPhil candidate in Cancer Science, conducting his doctoral research in the UK Biobank group at the Big Data Institute. His research focuses on developing multi-modal machine learning approaches to improve early cancer detection and risk prediction in the general population.
Registration- forms.office.com/e/ddQhg7pG2N?origin=lprLink
