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Invited sessions

Tentative overview:

Invited sessionsOrganizerSpeakersTopics
I1: Model evaluationThordis ThorarinsdottirClaudio HeinrichProper scoring rules for point processes
Jonas WallinLocally scale invariant proper scoring rules
I2: Causal learningNiels Richard Hansen Ingeborg WaernbaumProperties of calibration estimators of the average causal effect - a comparative study of balancing approaches
Leonard HenckelGraphical tools for selecting efficient conditional instrumental sets
Lasse PetersenCombining the partial copula with quantile regression to test conditional independence
I3: Network analysisLasse Leskelä
Maximilien DrevetonEstimation of static community memberships from multiplex and temporal network data
Alex JungNetworked Federated Multi-Task Learning
Fanny VillersMultiple testing of paired null hypotheses using a latent graph model
I4: BiostatisticsIain JohnstonMatti PirinenVariable selection using summary statistics
Alvaro Köhn-LuqueDeconvolution of drug-response heterogeneity in cancer cell populations
Owen ThomasLilleBror for misspecification-robust likelihood free inference in high dimensions
I5: Modeling spatial data -porous materials, proteins and networksAila SärkkäSandra BarmanPorous materials: spatial models of 3D geometries with specific global connectivity structures & new methods for capturing the connectivity
Louis Gammelgaard JensenSemiparametric point process modeling of blinking artifacts in photoactivated localization microscopy
Mohammad Mehdi MoradiP​oint patterns on linear networks: a focus on intensity estimation
I6: Statistics in forestryLauri MehtätaloJuha LappiBetween-group and within group effects and intra-class correlation
Mari MyllymäkiNonparametric graphical tests of significance for the functional general linear model with application to forestry data
Lauri MehtätaloFinding hidden trees in remote sensing of forests by using stochastic geometry, sequential spatial point processes and the HT-estimator
I7: Teaching and communicating statisticsMette Langaas
Thea BjørnlandDeveloping an introductory statistics course for engineering students at NTNU
Sam CliffordProject-based learning for statistics in practice - collaboration, computation, communication
Mine Çetinkaya-RundelThe art and science of teaching data science
I8: Recent advances in causal inferenceJuha KarvanenTetiana GorbachContrasting identification criteria of average causal effects: Asymptotic variances and semiparametric estimators
Niklas PfisterStabilizing variable selection and regression
Juha KarvanenIdentifying causal effects via context-specific independence relations
I9: Opening the black boxMartin JullumMartin JullumEfficient Shapley value explanation through feature groups
Kary FrämlingWhy explainable AI should move from influence to contextual importance and utility
Homayun Afrabandpey Model interpretability in Bayesian framework

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