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SUMMARY:Decision-making for workflows in computational biology\, using AI 
 and classical statistical approaches
DTSTART:20261007T103000Z
DTEND:20261007T113000Z
DTSTAMP:20261009T010300Z
UID:indico-event-1944@indico.stfc.ac.uk
DESCRIPTION:Speakers: David McDonagh & Robert Nicholls (SCD/RAL)\n\nModern
  computational science increasingly relies on complex workflows in which m
 any decisions must be made: which data to process\, which algorithms to ap
 ply\, how their parameters should be chosen\, and when a result is good en
 ough to proceed. While similar challenges arise across many areas of compu
 tational science\, they are important problems in computational biology\, 
 where workflows often involve many interdependent processing\, modelling a
 nd analysis steps.\nIn this seminar\, we will discuss early-stage ALC-fund
 ed projects at STFC exploring how we are addressing these problems within 
 computational biology\, using complementary approaches ranging from classi
 cal statistical methods to machine learning and AI. Information from previ
 ous computations can be used to understand the behaviour of scientific wor
 kflows\, predict outcomes\, and guide future decisions. Doing this reliabl
 y raises fundamental questions about how success should be defined and mea
 sured\, how uncertainty and heterogeneous data should be handled\, and how
  optimisation objectives can be aligned with the scientific outcomes we ac
 tually care about. Using examples from computational structural biology\,
  we will introduce the problems we are tackling\, the approaches we are de
 veloping\, and some of the challenges involved in making better-informed d
 ecisions within complex computational workflows.\n \n \n \n************
 *****************\n \nBiographies of the speakers:\n \n\nDavid McDonagh 
 and Rob Nicholls are Computational Scientists in STFC Scientific Computing
  at the Rutherford Appleton Laboratory\, working on methods\, software and
  data analysis for computational structural biology.\n \nDavid’s backgr
 ound is in crystal structure prediction\, electronic structure theory\, cr
 ystallography\, software engineering\, embedded devices and machine learni
 ng. He obtained a master’s in Natural Sciences at Leicester University\,
  a master’s in Theory and Modelling in the Chemical Sciences at Oxford U
 niversity\, and a PhD in Theoretical and Computational Chemistry at Southa
 mpton University. His current work combines scientific software developmen
 t with machine learning and AI to automate computational workflows.\n \nR
 ob studied Mathematics at the University of York\, followed by a master’
 s in Mathematics in the Living Environment and a PhD in Chemistry. He then
  spent over a decade at the MRC Laboratory of Molecular Biology in Cambrid
 ge\, developing methods and software for macromolecular crystallography an
 d cryo-electron microscopy. His research interests centre on statistical i
 nference\, modelling and data analysis\, and he also teaches Applied Stati
 stics at the University of Cambridge.\n\n \n\nhttps://indico.stfc.ac.uk/e
 vent/1944/
LOCATION:R61 CR03 (RAL)
URL:https://indico.stfc.ac.uk/event/1944/
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