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SUMMARY:IC Monday Seminar - "Metric structures on datasets: design\, stabi
 lity\, and classification of algorithms"
DTSTART:20110221T161500
DTSTAMP:20260414T233504Z
UID:6d61376ab02cc92c756b982c04d573e09e9f443c14edaf8803da8498
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Facundo Memoli\, Stanford University - IC Faculty candidate
 \nAbstract: \nSeveral methods in data and shape analysis can be regarded a
 s transformations between metric spaces\, or in general\, as maps from k-t
 uples of metric spaces into a metric space.  Examples are hierarchical clu
 stering methods\, the higher order constructions of computational persiste
 nt topology\, and several techniques that operate  within the context of d
 ata/shape matching under invariances. Metric geometry\, and in particular 
 variants of the Gromov-Hausdorff distance provide a point of view which is
  applicable in different scenarios. The underlying idea is to regard datas
 ets as metric spaces (or metric measure spaces)\, and then\, crucially\, a
 t the same time\, regard the collection of all datasets as a metric space 
 in itself. Variations of this point of view give rise to different taxonom
 ies that include several methods\, both preexisting and novel\, for extrac
 ting information from datasets and shapes. I will give and overview of the
 se constructions and describe an ongoing application of one of these new m
 ethods  to functional classification of chemical compounds by shape proper
 ties.\n\nBio : Facundo Mémoli is a postdoctoral fellow in the Mathematics
  \nDepartment at Stanford University. His research interests are in \nshap
 e and data analysis and in metric geometry. Facundo received his B.Sc. and
  M.Sc.\n in Electrical Engineering from the Universidad de la Republica in
  \nUruguay\, and his Ph.D. in Electrical Engineering from the University o
 f Minnesota
LOCATION:INM202
STATUS:CONFIRMED
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