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DTSTAMP:20220216T150333Z
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SUMMARY:Turing Fellow 'Spotlight': 1 March
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 2-335eew
DESCRIPTION:The Digital Futures team are hosting a series of online talks
  by The University of Manchester's new Turing Fellows for 2021-22.\n\nFo
 r the final event in this series\, new Turing Fellow Christopher Conseli
 ce is joined by Turing AI Fellow Anna Scaife.\n\nChristopher Conselice\,
  Professor of Extragalactic Astronomy.\n\nChristopher's research is focu
 sed on galaxy formation and evolution through cosmic time. He works with
  space and ground-based data and computer simulations to determine how g
 alaxies have formed from the earliest epochs in the universe until today
 . He has led several large HST surveys including the GOODS NICMOS Survey
  (GNS) and is a founding member of other large surveys including CANDELS
  and GOODS.\n\nChristopher's talk is titled: 'Machine learning applicati
 ons in astrophysics:  Large surveys and galaxy properties/evolution'\n\n
 Recently the application of machine learning\, especially deep learning\
 , to astronomy and astrophysics has become very popular.   Open use of l
 arge data sets in astronomy makes it ideal for applications of machine l
 earning\, yet many of the tools\, procedures\, and adaptability are stil
 l in their early stages. I will present some of our work on these topics
  including using supervised\, unsupervised\, and regression analyses for
  determining galaxy properties and evolution. I will also discuss upcomi
 ng astronomy missions which will contain hundreds of millions of galaxie
 s\, stars\, quasars\, defects and other detected objects that can only b
 e classified and studied using machine learning techniques that are in t
 he process of being developed.\n\nAnna Scaife\, Professor of Radio Astro
 nomy at Jodrell Bank Centre for Astrophysics.\n\nAnna’s Turing AI Fellow
 ship focuses on AI for discovery in data intensive astrophysics. In this
  era of big data astrophysics\, the use of machine learning to extract s
 cientific information is essential to successfully utilise facilities su
 ch as the Square Kilometre Array (SKA) telescopes. These telescopes have
  data rates so large that the raw data cannot be stored and even using t
 he compressed data products will require a super-computer.\n\nAnna's tal
 k is titled 'AI in the SKA Era: Challenges for Bayesian Neural Networks 
 in Radio Galaxy Classification'.\n\nThe expected volume of data from the
  new generation of scientific facilities such as the Square Kilometre Ar
 ray (SKA) radio telescope has motivated the expanded use of semi-automat
 ic and automatic machine learning algorithms for scientific discovery in
  astronomy. In this field\, the robust and systematic use of machine lea
 rning faces a number of specific challenges including a paucity of label
 led data for training (paradoxically\, although we have too much data\, 
 we don't have enough)\, a clear understanding of the effect of biases in
 troduced due to observational and intrinsic astrophysical selection effe
 cts in the training data\, and motivating a quantitative statistical rep
 resentation of outcomes from decisive AI applications. In this seminar I
  will talk specifically about the challenge of recovering well-calibrate
 d uncertainties from Bayesian neural networks when classifying radio gal
 axies\, a canonical example of a radio astronomy AI application. I will 
 discuss how both model and likelihood misspecification can affect this c
 alibration\, how these effects potentially contribute to the cold poster
 ior effect seen when building models using real astronomical data and wh
 at steps we can take to address these problems.
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