BEGIN:VCALENDAR
PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20251023T070228Z
DTSTART:20251210T110000Z
DTEND:20251210T120000Z
SUMMARY:AI-Fun & ELLIS Invited Speaker Series | Sam Power
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}u102-mgpcdh
 2y-mc5tku
DESCRIPTION:November’s AI Fun and ELLIS Invited Speaker is Sam Power from
  the University of Bristol\n\nBio: Sam is a Lecturer in the School of Ma
 thematics. Sam received his PhD degree in 2020 from the Statistical Labo
 ratory at the University of Cambridge\, where his thesis focused on the 
 synthesis and analysis of stochastic simulation algorithms for Bayesian 
 inference. He then joined the School of Mathematics at Bristol as a post
 doctoral research associate\, further developing his interests in statis
 tical modelling and computation\, before securing a role as Lecturer.\n\
 nHis research interests at present centre around i) theoretical aspects 
 of Markov chain Monte Carlo (MCMC) algorithms\, and ii) scalable computa
 tional methods for state-space models\, with applications to inference i
 n stochastic epidemic models\, though he retains broad interests across 
 aspects of applied probability\, statistical inference\, and computation
 .\n\nTitle: A State-Space Perspective on Modelling and Inference for Onl
 ine Skill Rating\n\nAbstract: In the quantitative analysis of competitiv
 e sports\, a fundamental task is to estimate the skills of the different
  agents (‘players’) involved in a given competition based on the outcome
  of pairwise comparisons (‘matches’) between said players\, often in an 
 online setting. In this talk\, I will discuss recent work in which we ad
 vocate for adoption of the state-space modelling paradigm in solving thi
 s problem. This perspective facilitates the decoupling of modeling from 
 inference\, enabling a more focused approach to development and critique
  of model assumptions\, while also fostering the development of general-
 purpose inference tools.   \n\nI will first describe some illustrative m
 odel classes which arise in this framework\, before turning to a careful
  discussion of inference and computation strategies for these models. A 
 key challenge throughout is to develop methodology which scales graceful
 ly to problems with a large number of players and a high frequency of ma
 tches. I then conclude by describing some real-data applications of our 
 approach\, demonstrating how this framework facilitates a practical work
 flow across different sports.\n\nThis is joint work with Samuel Duffield
  (Normal Computing) and Lorenzo Rimella (Università degli Studi di Torin
 o).\n\nIn-person attendance is encouraged but if you can only join onlin
 e\, you are welcome to register via Ticket Source (link provided on this
  page).
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Lecture Theatre 1.4\, Kilburn Building\, Manchester
END:VEVENT
END:VCALENDAR