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Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)

Authors: Frey, Jeremy;

Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)

Abstract

This video is the sixth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton) Bio: Prof Jeremy Frey Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton (UoS). He is PI of AI for Scientific Discovery Network+, and co_I on the Internet of Food Things Digital Economy Network+ and has had considerable involvement in the UK e-Science and Digital Economy programmes for many years (e.g., PI of the Digital Economy IT as a Utility Network+. He is a strong proponent of interdisciplinary research and the use of digital technology and ideas to enhance methods of scientific research & development. His own research involves activities across the physical land life sciences, from the application of novel mathematical analysis (e.g., Topological Data Analysis), laser spectroscopy and imagining techniques to chemical and biological problems, with the development of sensors and imagining systems such as the novel soft x-ray microscope. In parallel he works on the integration of these techniques with full provenance environment into laboratory systems using semantic web technologies. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/eNORwfMy5cE

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Keywords

machine learning, data science, artificial intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average