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VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20240416T152230Z
DTSTART:20240508T093000Z
SUMMARY:AI-Fun Seminar | Neill Campbell: Generative models as priors for 
 inverse problems
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}w38-lv262su
 m-vxl3hw
DESCRIPTION:The Manchester Centre for AI Fundamentals is hosting a series
  of seminars featuring expert researchers working in the fundamentals of
  AI. \n\nTitle: Generative models as priors for inverse problems.\n\nAbs
 tract: We will discuss the use of generative models in the regularisatio
 n of inverse problems involving both appearance and shape as well as the
  merits of integrating probabilistic approaches. The different natures o
 f appearance and shape motivate the need for fundamentally different mod
 elling approaches and we will contrast effective models as well as consi
 der how to unify them towards a universal modelling framework that gener
 alises to a range of desirable properties. We will illustrate the utilit
 y of this methodology across a range of real-world problems from medical
  imaging to the creative industries.\n\nBio: Neill Campbell is a Royal S
 ociety Industry Fellow and Professor of Visual Computing and Machine Lea
 rning in the Department of Computer Science at the University of Bath. H
 e is the director of the Centre for the Analysis of Motion\, Entertainme
 nt Research and Applications (CAMERA) that researches and applies visual
  computing and machine learning technology in the fields of Entertainmen
 t\, Health and Sports Science\; chair of the British Machine Vision Asso
 ciation\; and co-director of the Centre for Mathematics and Algorithms f
 or Data\, an inter-disciplinary group that studies the theoretical under
 pinnings of Machine Learning and Data Science\; and the director of rese
 arch for MyWorld\, a creative hub across the Bath and Bristol region\, f
 unded by UKRI and an alliance of more than 30 industry and academic part
 ners. Before moving to Bath he worked as a post-doc at UCL and Cambridge
 \, where he also completed his PhD in Computer Vision. His research invo
 lves learning models of shape\, appearance and dynamics from images and 
 video. In particular\, creating systems that do not require technical co
 mputing expertise (e.g. for artists). He also works on machine learning 
 problems where data are scarce or expensive to obtain (e.g. annotations 
 from expert clinicians) and when uncertainty in the resulting output is 
 important (e.g. medical and safety applications).
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Emmeline Suite\, Christabel Pankhurst Building\, Dover Street \,
  Manchester
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