| I1: Model evaluation | Thordis Thorarinsdottir | Claudio Heinrich | Proper scoring rules for point processes |
| Jonas Wallin | Locally scale invariant proper scoring rules |
| I2: Causal learning | Niels Richard Hansen | Ingeborg Waernbaum | Properties of calibration estimators of the average causal effect - a comparative study of balancing approaches |
| Leonard Henckel | Graphical tools for selecting efficient conditional instrumental sets |
| Lasse Petersen | Combining the partial copula with quantile regression to test conditional independence |
| I3: Network analysis | Lasse Leskelä
| Maximilien Dreveton | Estimation of static community memberships from multiplex and temporal network data |
| Alex Jung | Networked Federated Multi-Task Learning |
| Fanny Villers | Multiple testing of paired null hypotheses using a latent graph model |
| I4: Biostatistics | Iain Johnston | Matti Pirinen | Variable selection using summary statistics |
| Alvaro Köhn-Luque | Deconvolution of drug-response heterogeneity in cancer cell populations |
| Owen Thomas | LilleBror for misspecification-robust likelihood free inference in high dimensions |
| I5: Modeling spatial data -porous materials, proteins and networks | Aila Särkkä | Sandra Barman | Porous materials: spatial models of 3D geometries with specific global connectivity structures & new methods for capturing the connectivity
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| Louis Gammelgaard Jensen | Semiparametric point process modeling of blinking artifacts in photoactivated localization microscopy |
| Mohammad Mehdi Moradi | Point patterns on linear networks: a focus on intensity estimation |
| I6: Statistics in forestry | Lauri Mehtätalo | Juha Lappi | Between-group and within group effects and intra-class correlation |
| Mari Myllymäki | Nonparametric graphical tests of significance for the functional general linear model with application to forestry data |
| Lauri Mehtätalo | Finding hidden trees in remote sensing of forests by using stochastic geometry, sequential spatial point processes and the HT-estimator |
| I7: Teaching and communicating statistics | Mette Langaas
| Thea Bjørnland | Developing an introductory statistics course for engineering students at NTNU
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| Sam Clifford | Project-based learning for statistics in practice - collaboration, computation, communication |
| Mine Çetinkaya-Rundel | The art and science of teaching data science |
| I8: Recent advances in causal inference | Juha Karvanen | Tetiana Gorbach | Contrasting identification criteria of average causal effects: Asymptotic variances and semiparametric estimators |
| Niklas Pfister | Stabilizing variable selection and regression |
| Juha Karvanen | Identifying causal effects via context-specific independence relations |
| I9: Opening the black box | Martin Jullum | Martin Jullum | Efficient Shapley value explanation through feature groups |
| Kary Främling | Why explainable AI should move from influence to contextual importance and utility |
| Homayun Afrabandpey | Model interpretability in Bayesian framework |
