spring_2026

Cassandra Durr is a PhD candidate at the STOR-i Centre for Doctoral Training at Lancaster University, UK. Her research focuses on non-linear system identification through equation discovery of physical systems, with an emphasis on epidemic modelling. Cassandra holds a bachelor’s degree in Actuarial Sciences and postgraduate degrees in Mathematical Statistics, and Machine Learning & Artificial Intelligence.

Aliaksandr Hubin is an Associate Professor in Statistics at the Norwegian University of Life Sciences and University of Oslo. He holds a PhD in Statistics from the University of Oslo (2018) and specializes in Bayesian inference, machine learning, and statistical modeling. His research focuses on scalable and interpretable methods in Bayesian regression context, with particular expertise in latent binary Bayesian neural networks, Bayesian generalized nonlinear models.

Andrew is a postdoctoral researcher in Statistical Machine Learning at the University of Limerick, Ireland. His research interests centre on developing methods at the intersection of statistical modelling and machine learning. He completed his PhD in 2024 where his research focused on statistically-based methods for neural networks, with a particular focus on model selection and interpretation. After his PhD, he spent some time in the pharmaceutical industry, working on Phase 3 clinical trials in the immunology therapeutic area.

Olav Nikolai Breivik is a researcher at the Norwegian Computing Center, where he mainly works on applying statistical methodology to marine science, as well as other projects in applied statistics. He earned his Ph.D. in statistics from the University of Oslo under the supervision of Ida Scheel, Geir Storvik, Arne Bang Huseby and Bent Natvig.

Rebecca Killick received their PhD degree in Statistics from Lancaster University, where they hold Professor and Director of Research positions. In 2019 they were the first UK recipient of the “Young Statistician of the Year” award from the European Network for Business and Industrial Statistics which recognizes the work of young people in introducing innovative methods, promoting the use of statistics and/or successfully using it in daily practice. Rebecca sees their research as a feedback loop, being inspired by problems in real world applications, creating novel methodology to solve those problems and then feeding these back into the problem domain. Their primary research interests lie in development of novel methodology for the analysis of univariate and multivariate nonstationary time series models. This covers many topics including developing models, model selection, efficient estimation, diagnostics, clustering and prediction. Rebecca is highly motivated by real world problems and has worked with data in a range of fields including Bioinformatics, Energy, Engineering, Environment, Finance, Health, Linguistics and Official Statistics. Rebecca is passionate about ensuring the availability and accessibility of research in the form of open-source software. As part of this they advocate to the statistical community the importance of recognition of research software as an academic output, are co-Editor in Chief of the Journal of Statistical Software and a member of the rOpenSci statistical software peer review board.

William R.P. Denault is a researcher at OCBE-OUS where he focuses on statistical genetics. He got his Ph.D. at the University of Bergen under the supervison of Haakon Gjessing and Astanand Jugessur.