KeyNOTE speakers

Su is Professor of Neuroimaging at the University of Sheffield. He also holds a part-time PI position in the Department of Psychiatry at the University of Cambridge. He leads the Alzheimer’s Research UK East Network Centre and is an elected Fellow in Clare Hall, University of Cambridge. His research has been recognised through international awards including an International College of Geriatric Psychoneuropharmacology Junior Investigator Award in 2015. More details are available at https://www.sheffield.ac.uk/smph/people/neuroscience/li-su.

Topic: AI in Basic and Clinical Neuroscience 

Abstract: Traditionally, neural networks have been used for data analysis and as models of the mind and the brain. These two areas have both made historically significant contributions. For example, connectionism as a model of the brain has helped cognitive psychologists to understand many computational principles in language acquisition, memory and control of action. Although modern AI inherited many fundamental structures and features in conventional neural network models, its current applications in neuroscience have primarily been data analysis, e.g. for MRI data. In this talk, I will show how modern AI can contribute to both data analysis in neuroscience and also help theorising computational principles implemented by the brain. 

Yau is Professor of Artificial Intelligence based at the Big Data Institute in Oxford working across the Nuffield Department of Women's and Reproductive Health and the Nuffield Department of Population Health. He is a Turing AI Fellow and his research is supported by a UKRI/EPSRC Turing AI Acceleration Fellowship. Outside of Oxford, he is also a PhD Programme Director at Health Data Research UK, leading the Health Data Research UK-Turing Wellcome PhD programme in Health Data Science. More details at https://www.bdi.ox.ac.uk/Team/christoper-yau. 

Topic: A machine learning take on some biostatistical problems 

Abstract: There are many for whom there is a strict distinction between Statistics and Machine Learning yet there are many interactions between the disciplines which have led to new insights into real-world data modelling. In this talk, I will describe some examples of problems arising from biomedical and health applications and the benefits of blending statistical and machine learning approaches in developing solutions. This will include the use of neural-based approaches for functional decomposition and survival analysis and attention-based multiresolution signal analysis for cancer genome characterisation.