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UID:637@fds.yale.edu
DTSTART;TZID=America/New_York:20241030T113000
DTEND;TZID=America/New_York:20241030T133000
DTSTAMP:20250916T142141Z
URL:https://fds.yale.edu/events/fds-colloquium-houman-owhadi-caltech/
SUMMARY:FDS Colloquium: Houman Owhadi (Caltech)\, "Co-discovering graphica
 l structure and functional relationships within data: A Gaussian Process 
 framework for connecting the dots"
DESCRIPTION:\nAbstract:&nbsp\;Most scientific challenges can be framed into
  one of the following three levels of complexity of function approximation
 .&nbsp\;\n\n\n\n\nType 1: Approximate an unknown function given input/outp
 ut data.&nbsp\;\n\n\n\nType 2: Consider a collection of variables and func
 tions\, some of which are unknown\, indexed by the nodes and hyperedges of
  a hypergraph (a generalized graph where edges can connect more than two v
 ertices). Given partial observations of the variables of the hypergraph (s
 atisfying the functional dependencies imposed by its structure)\, approxim
 ate all the unobserved variables and unknown functions.&nbsp\;\n\n\n\nType
  3: Expanding on Type 2\, if the hypergraph structure itself is unknown\, 
 use partial observations of the variables of the hypergraph to discover it
 s structure and approximate its unknown functions.&nbsp\;\n\n\n\n\nExample
 s of Type 2 problems include solving and learning (possibly stochastic) no
 nlinear partial differential equations (PDEs)\, while Type 3 problems enco
 mpass learning dependencies between variables in a mechanical system\, ide
 ntifying chemical reaction networks\, and determining relationships betwee
 n genes through a protein-signaling network. Although Gaussian Process (GP
 ) methods are sometimes perceived as a well-founded but old technology lim
 ited to Type 1 curve fitting\, they can be generalized to an interpretable
  framework for solving Type 2 and Type 3 problems\, all while maintaining 
 the simple and transparent theoretical and computational guarantees of ker
 nel/optimal recovery methods.\n\n\n\nBio: Professor Owhadi's research conc
 erns the exploration of interplays between numerical approximation\, stati
 stical inference and learning&nbsp\; from a game theoretic perspective. Wh
 ereas the process of discovery is usually based on a combination of trial 
 and error\,&nbsp\; insight and plain guesswork\, his research is motivated
  by the facilitation/automation possibilities&nbsp\; emerging from these i
 nterplays.\n\n\n\nWebsite: https://www.cms.caltech.edu/people/owhadi\n
ATTACH;FMTTYPE=image/jpeg:https://fds.yale.edu/wp-content/uploads/2024/09/
 Houman_Owhadi.jpeg
CATEGORIES:FDS Events,Colloquium
LOCATION:Yale Institute for Foundations of Data Science\, Kline Tower 13th 
 Floor\, Room 1327\, New Haven\, CT\, 06511\, United States
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