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SUMMARY:Fantastic ML x Biology Problems and Where to Find Them - Dr. Simon
  Kohl (Latent labs & former Deepmind AlphaFold team)
DTSTART:20240122T174500Z
DTEND:20240122T184500Z
UID:TALK211111@talks.cam.ac.uk
CONTACT:Simon Mathis
DESCRIPTION:*Abstract:*\n\nAlphaFold2 was recognized for its ability to ri
 val the structural accuracy of experimental methods on single chain protei
 n structures. How is it that this model is so successful\, how was it deve
 loped and are there other similar problems out there that wait to be solve
 d with machine learning?\nThis talk attempts to identify some of the prope
 rties required for machine learning to be successful in a biological appli
 cation. The second goal of the talk is to give a (selected) view of the cu
 rrent status of machine learning in biology and finally\, to highlight the
  many remaining opportunities in this area.\n\n\n\n*Speaker Bio:*\n\nSimon
  is the founder of Latent Labs\, a company developing generative foundatio
 n models for all molecules of life. Latent’s mission is to make syntheti
 c biology programmable. The team is currently based in London and joined b
 y other former members of the AlphaFold2 and DeepMind Science team.\nSimon
  has co-led DeepMind’s protein design team and set up DeepMind’s wet l
 ab at the Francis Crick Institute in London. Before that\, he was a member
  of the AlphaFold2 team\, where he contributed to the core deep learning a
 lgorithm\, including developing the uncertainty estimate that is now widel
 y known as pLDDT.\n\n*Online link:*\nhttps://meet.google.com/eod-stjw-fwe\
 n
LOCATION:Department of Computer Science (Room TBA\, meet at reception for 
 now)
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