fall_2024

Clara Bertinelli Salucci is a Postdoctoral Fellow at the Department of Mathematics, affiliated with the PLUMBIN' (Physics Learning Using Machines and Bayesian INference) project at the University of Oslo. She completed her PhD in Statistics and Machine Learning at the University of Oslo in April 2024, where she developed statistical and machine learning methods for diagnostics and prognostics of lithium-ion batteries for maritime applications. Currently, she is investigating the role of likelihood ratio test statistics for hypotheses tests in experimental and theoretical particle physics, focusing on deviations from Wilks' theorem in the search for new particles. Her postdoctoral research combines her expertise in statistics with her background in high-energy physics.

Dr Kevin Burke is an Associate Professor of Statistics at the University of Limerick (UL) with research interests in flexible statistical modelling, penalised regression, neural networks, random effects, survival/reliability analysis, and industrial modelling. See https://kevinburke.ie/ for more details. Previously he was Director of UL's Industrial Mathematics Unit (from 2016 - 2020), and a Science Foundation Ireland Public Service Fellow with the Food Safety Authority of Ireland (2020 - 2021). He is the Vice President of the Irish Statistical Association, and the Chair of the International Workshop on Statistical Modelling (IWSM) 2025. IWSM 2025 will be hosted in Limerick City, Ireland, from Sunday 13th July to Friday 18th July 2025; the call for papers is currently open             (https://iwsm2025.ie/call-for-papers).

Leiv Rønneberg is a DSTrain Postdoctoral Fellow at the Department of Mathematics and affiliated with The Norwegian Centre for Knowledge-driven Machine Learning (INTEGREAT) at the University of Oslo. Prior to this he was a Research Associate at the MRC Biostatistics Unit at the University of Cambridge, and completed his PhD in Biostatistics at the University of Oslo in 2022. His interests are broadly within the realms of probabilistic machine learning, and connections to Bayesian nonparametrics.
 

Thomas Nagler is a Professor of Computational Statistics \& Data Science at the Department of Statistics, LMU Munich, and a principal investigator at the Munich Center for Machine Learning (MCML).

Previously, he held positions as an assistant professor at TU Delft and Leiden University, following a PhD in Mathematical Statistics from TU Munich.

His research lies at the intersection of mathematical and computational statistics. His group develops statistical methods, establishes theoretical guarantees, and designs scalable algorithms, which are packaged into user-friendly software. He also regularly collaborates with domain experts to tackle challenges in diverse application areas.

Håkon Hoel got his PhD in Numerical Analysis from KTH Stockholm in 2012. He is currently an Associate Professor in Computational Mathematics at the University of Oslo. His main research interests are Monte Carlo methods and numerical methods for stochastic differential equations and data assimilation. 

Mikko has been working at Natural Resources Institute Finland (Luke) since 2017.
He finished his PhD on spatial point processes in 2022 (University of Jyväskylä).
Since then Mikko has been working on many kinds of remote sensing, forest inventory and forest planning projects.
Also, during 2015-2016 Mikko was working at the National Institute of Health and Welfare as a data manager.