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UID:20251021T203911EDT-4018bTMju2@132.216.98.100
DTSTAMP:20251022T003911Z
DESCRIPTION:Title:Learning Connectivity Networks from High-Dimensional Poin
 t Processes.\n\n\n	Abstract:\n\n\nHigh-dimensional point processes have bec
 ome ubiquitous in many scientific fields. For instance\, neuroscientists u
 se calcium florescent imaging to monitor the firing of thousands of neuron
 s in live animals. In this talk\, I will discuss new methodological\, comp
 utational and theoretical developments for learning neuronal connectivity 
 networks from high-dimensional point processes. Time permitting\, I will a
 lso discuss a new approach for handling non-stationarity in high-dimension
 al time series.\n\n\n	Speaker\n\n\nAli Shojaie is an Associate Professor of
  Biostatistics\, and Adjunct Associate Professor of Statistics in the Depa
 rtment of Biostatistics at the University of Washington. His research focu
 ses on statistical machine learning\, high-dimensional networks\, estimati
 on and inference in high-dimensional problems.\n
DTSTART:20191025T193000Z
DTEND:20191025T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Ali Shojaie (University of Washington)
URL:/mathstat/channels/event/ali-shojaie-university-wa
 shington-301988
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