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SUMMARY:Nina van Gerwen from Erasmus University - Statistical Intelligence
 : Machine Learning with Statistics for Longitudinal Data Analysis
DTSTART:20261124T120000Z
DTEND:20261124T130000Z
DTSTAMP:20261110T173000Z
UID:indico-event-17344@indico.math.cnrs.fr
CONTACT:denis.rustand@u-bordeaux.fr
DESCRIPTION:Speakers: Nina van Gerwen\n\nSpeaker: Nina van Gerwen\nTitle: 
 Statistical Intelligence: Machine Learning with Statistics for Longitudina
 l Data Analysis\nAbstract: During my PhD\, I have focused on different way
 s that statistics and machine learning can inspire one another. This has l
 ed to a total of three projects\, each tailored to a specific longitudinal
  research setting/question. In my first project\, I researched how ensembl
 e learning can be used as an alternative to model selection procedures for
  the analysis of longitudinal and time-to-event data to combine landmarkin
 g with joint modeling. For the next project\, I generalised mixed effects 
 models to deep learning\, trying to achieve the benefits of both statistic
 s and machine learning. This led to designing a new architecture for hiera
 rchical data analysis called the ‘Deep Generalised Mixed Model’. Lastl
 y\, I am currently working on a deep learning method inspired by the Deep 
 Generalised Mixed Model and latent class mixed models to find categories w
 ithin trajectories of high-frequency longitudinal data.\nCalendar subscrip
 tion link for the complete seminar series:https://indico.math.cnrs.fr/cate
 gory/711/events.ics\nProgram of the Biostatistics seminars:https://indico.
 math.cnrs.fr/category/711/\nSubscribe to the seminar mailing list:https://
 diff.u-bordeaux.fr/sympa/subscribe/seminaire.biostat.bph\nFormer e-seminar
 s on our YouTube channel (mostly in French): https://www.youtube.com/chann
 el/UCURp-hEQL7k23UzGfqgEurA/videos\n \nBiostatistics seminar series from 
 the Department of Public Health from the University of Bordeaux and the Bo
 rdeaux Population Health UMR 1219 research center\n \n\nhttps://indico.ma
 th.cnrs.fr/event/17344/
LOCATION:amphi 8 (ISPED)
URL:https://indico.math.cnrs.fr/event/17344/
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