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UID:20251024T091433EDT-1738dJZvLv@132.216.98.100
DTSTAMP:20251024T131433Z
DESCRIPTION:Title: Estimation and inference for changepoint models.\n\n\n	Ab
 stract: This talk is motivated by statistical challenges that arise in the
  analysis of calcium imaging data\, a new technology in neuroscience that 
 makes it possible to record from huge numbers of neurons at single-neuron 
 resolution. In the first part of this talk\, I will consider the problem o
 f estimating a neuron’s spike times from calcium imaging data. A simple an
 d natural model suggests a non-convex optimization problem for this task. 
 I will show that by recasting the non-convex problem as a changepoint dete
 ction problem\, we can efficiently solve it for the global optimum using a
  clever dynamic programming strategy.\n\n\nIn the second part of this talk
 \, I will consider quantifying the uncertainty in the estimated spike time
 s. This is a surprisingly difficult task\, since the spike times were esti
 mated on the same data that we wish to use for inference. To simplify the 
 discussion\, I will focus specifically on the change-in-mean problem\, and
  will consider the null hypothesis that there is no change in mean associa
 ted with an estimated changepoint. My proposed approach for this task can 
 be efficiently instantiated for changepoints estimated using binary segmen
 tation and its variants\, L0 segmentation\, or the fused lasso. Moreover\,
  this framework allows us to condition on much less information than exist
 ing approaches\, thereby yielding higher-powered tests. These ideas can be
  easily generalized to the spike estimation problem.\n\nThis talk will fea
 ture joint work with Toby Hocking\, Paul Fearnhead\, and Daniela Witten.\n
 \nBrief Bio: Sean Jewell is a PhD Candidate in the Department of Statistic
 s at the University of Washington\, mentored by Daniela Witten. He complet
 ed his MSc in Statistics in the Department of Statistics at the University
  of British Columbia under the supervision of Alexandre Bouchard-Côté. His
  undergraduate degree was in Applied Mathematics from McMaster University.
  He is the recipient of an NSERC Alexander Graham Bell Doctoral Scholarshi
 p and the Z.W. Birnbaum Award from UW’s Department of Statistics.\n\nHis r
 esearch focuses on developing new methods for applied problems using tools
  from high-dimensional statistics\, machine learning\, and optimization.\n
DTSTART:20200113T203000Z
DTEND:20200113T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Sean Jewell (University of Washington)
URL:/mathstat/channels/event/sean-jewell-university-wa
 shington-303427
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