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SUMMARY:Workshop "Stochastic Analysis\, Statistics\, and Computation on Ma
 nifolds and Singular Spaces" [HIM Workshop]
DTSTART:20261012T070000Z
DTEND:20261016T150000Z
DTSTAMP:20260721T193800Z
UID:indico-event-492@math-events.uni-bonn.de
CONTACT:him-coordination@hcm.uni-bonn.de\;him-contact@hcm.uni-bonn.de
DESCRIPTION:Speakers: Kanami Ueda\, Emma Seggewiss (HCM)\n\nThis workshop 
 is directed at the participants in the Dual Trimester Program "Geometric S
 tatistics: theory\, application\, and computation". It is not possible to 
 apply only for this workshop. \nResearchers from the HCM\, in particular\
 , early-career researchers\, are welcome upon request.\nOrganizers: \n\nS
 hreya Arya (University of Pennsylvania)\nKaren Habermann (University of Wa
 rwick)\nStephan Huckemann (University of Göttingen)\nEzra Miller (Duke Un
 iversity)\nYvo Pokern (University College London)\nWilderich Tuschmann (Ka
 rlsruhe Institute of Technology)\nZhigang Yao (National University of Sing
 apore)\n\nDescription: \nAn increasing amount of modern data naturally li
 ves on curved\, constrained\, or stratified spaces\, including spaces with
  singularities. Classical statistical methodology often falls short whe
 n faced with curvature effects\, non‑smooth structure\, or singular beh
 aviour. Deepening our understanding of stochastic processes on both smoot
 h and singular spaces and adapting statistical methodology for such proce
 sses are therefore paramount for capturing modern data’s geometric varia
 bility and for advancing the theoretical foundations of geometric statisti
 cs. \nThis workshop aims to bring together researchers working at the int
 erface of stochastic analysis\, geometric statistics\, and computation on 
 manifolds and singular spaces. The central focus of the workshop is the st
 udy of stochastic processes on manifolds and\, crucially\, on singular spa
 ces\, where standard techniques and standard statistical methodology may b
 reak down yet many real‑world datasets naturally live in. By improving o
 ur understanding of stochastic processes and statistical models in these s
 ettings\, we aim to enable more robust statistical methods capable of hand
 ling complex geometric variability. Such developments will drive progress 
 in geometric statistics\, with applications in shape analysis\, computatio
 nal anatomy\, topological data analysis\, and machine learning on structur
 ed domains.\n\nhttps://math-events.uni-bonn.de/event/492/
LOCATION:Poppelsdorfer Allee 45\, 1. EG\, Lecture room (HIM)
URL:https://math-events.uni-bonn.de/event/492/
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