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BEGIN:VEVENT
DTSTAMP:20230117T132708Z
DTSTART:20230307T140000Z
DTEND:20230307T150000Z
SUMMARY:Advances in Data Science and AI | Welcome talks: Simon Rudkin and
  Xian Yang
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}qsp-lcusn32
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DESCRIPTION:In this new seminar sub-series\, our new academic colleagues 
 working in data science and AI-related areas introduce themselves to the
  IDSAI community with short talks on their current research.\n\nOn 7 Mar
 ch\, Simon Rudkin and Xian Yang will talk about their latest research. T
 his will be a hybrid event\, taking place at The University of Mancheste
 r and online.\n\nSimon Rudkin | Inference on Multi-Dimensional Data with
  Topological Data Analysis Ball Mapper\nThe goal of statistical modellin
 g is to provide the best fit to an outcome surface which extends across 
 a joint distribution of explanatory factors. Anscombe (1973)’s quartet o
 f datasets demonstrate the pitfalls of applying a univariate linear mode
 l without visualising the behaviour of the outcome across the distributi
 on of the explanatory factors. Topological Data Analysis Ball Mapper (TD
 ABM) produces visualisations and metrics that represent the joint distri
 bution of the characteristics of each data point. The space is covered b
 y a set of balls which represent points with similar characteristics. Ba
 lls may then be coloured according to functions on the data points withi
 n the ball. For example\, the function may be the average value of an ou
 tcome of interest. Consequently\, it becomes possible to talk about join
 t-density across the space\, identify subspaces where outcomes are obser
 vably different and conduct localised statistical analyses. This talk in
 troduces the TDABM algorithm\, presents examples and highlights the rese
 arch agenda for deriving inference on multi-dimensional data with TDABM.
 \n\nBio: Simon Rudkin is a Senior Lecturer in Data Science based within 
 the Social Statistics Department. His research focuses on the informatio
 n which is held within data and the ability to use that information for 
 societal benefit. Much of Simon’s research focuses on the development of
  Topological Data Analysis (TDA) for understanding data in the social sc
 iences and humanities. His work has considered applications in the UK\, 
 China\, Europe\, and the USA. Topics covered include the health impacts 
 of supermarkets\, regional productivity\, the digital economy\, and fina
 nce. He welcomes applications for PhD research on any application where 
 the improved use of statistical methodologies may answer research questi
 ons as yet not fully understood.\n\nXian Yang | Towards personalized hea
 lthcare: AI-based approaches\nPrecision medicine aims to provide persona
 lized healthcare for individual patients based on their health condition
 s. In the field of precision medicine\, electronic health records (EHRs)
  are becoming increasingly important in understanding patients' health c
 onditions and making clinical decisions. However\, analysing diverse\, h
 igh-dimensional\, and long-term EHRs can be challenging. This presentati
 on will explore various AI models applied to EHR data for precision medi
 cine. Important tasks within precision medicine will be discussed\, incl
 uding patient cohort selection\, disease risk prediction\, and drug reco
 mmendation. Models utilizing multi-modal learning\, natural language pro
 cessing\, and graph neural networks will be presented as solutions for t
 hese precision medicine tasks.\n\nBio: Xian Yang is currently a lecturer
  in Data Science at Alliance Manchester Business School (AMBS)\, The Uni
 versity of Manchester. Prior to joining AMBS\, she worked as an Assistan
 t Professor at the Department of Computer Science at HKBU\, a researcher
  at Microsoft Research Asia\, and a research fellow at the Data Science 
 Institute of Imperial College London. In 2016\, Dr. Yang received her Ph
 D from the Department of Computing at Imperial College London. Her resea
 rch interests include artificial intelligence in healthcare\, natural la
 nguage processing\, data mining\, and computational epidemiology. She ha
 s published her work in several top-tier conferences and journals such a
 s ICDM\, WWW\, FSE\, COLING\, and EMNLP.\n\nThis event has been organise
 d by the Institute for data Science and AI.\n\nIDSAI is one of The Unive
 rsity of Manchester's Digital Futures network themes.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:6.207\, University Place\, Manchester
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