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UID:DSC-22662
DTSTART;TZID=Europe/Berlin:20260227T110000
SEQUENCE:1772174273
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20260227T120000
URL:https://www.dresden-science-calendar.de/calendar/de/detail/22662
LOCATION:MPI-CBG\, Pfotenhauerstraße 10801307 Dresden
SUMMARY:Ruehle: From Physical Landscapes to Biological Design: Learning Org
 anizing Principles with AI
CLASS:PUBLIC
DESCRIPTION:Speaker: Fabian Ruehle\nInstitute of Speaker: Northeastern Univ
 ersity\, Dana Research Center\, Boston\nTopics:\n\n Location:\n  Name: MPI
 -CBG (MPI-CBG CBG Large Auditorium)\n  Street: Pfotenhauerstraße 108\n  C
 ity: 01307 Dresden\n  Phone: +49 351 210-0\n  Fax: +49 351 210-2000\nDescr
 iption: Modern molecular and multicellular biology is confronting the same
  conceptual bottlenecks that defined the great unsolved problems in theore
 tical physics: vast structured spaces of possibilities\, dynamical princip
 les underlying emergent behavior\, and the need for interpretable framewor
 ks rather than purely phenomenological fits. In physics\, the string lands
 cape demanded methods to navigate exponentially large solution spaces\, ef
 fective field theory provided tools to extract governing equations at the 
 right level of description\, and dualities revealed that apparently distin
 ct theories can encode the same physics. In biology\, similarly enormous c
 ombinatorial spaces arise in perturbation screens\, gene regulatory networ
 ks\, tissue morphogenesis\, and the design of emergent cellular systems. I
  will present a research program developed at the interface of mathematics
 \, theoretical physics and machine learning\, and argue that its methods a
 re naturally suited to discovering organizing principles in biological dat
 a: navigate large landscapes with sparse samples\, extract effective\, int
 erpretable laws directly from data\, recognize equivalence classes of mech
 anisms rather than unique solutions\, and invert design problems to genera
 te candidate protocols for target phenotypes. I will argue that these appr
 oaches can be directly applied to key open questions in molecular biology\
 , single-cell dynamics\, organoid screens\, morphogenesis\, and programmab
 le multicellular systems. This framework bridges physics\, mathematics\, m
 achine learning\, and biology\, and has immediate opportunities for collab
 oration with the research groups at MPI-CBG.
DTSTAMP:20261010T075929Z
CREATED:20260213T051023Z
LAST-MODIFIED:20260227T063753Z
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