Geir Olve Storvik

Geir Olve Storvik is a Professor in Statistics and Data Science at the University of Oslo. His research focuses on computational statistics, Bayesian hierarchical modeling, Monte Carlo methods, spatio-temporal modeling, and dynamical processes. He has made significant contributions to Bayesian machine learning and its applications in biological and environmental domains.

• University: University of Oslo
• Department: Statistics and Data Science
• Academic Rank: Professor

Storvik's research spans interdisciplinary applications of Bayesian methods in fields such as neural networks, DNA methylation, and infectious disease modeling. His recent work includes sparse Bayesian neural networks and sequential Monte Carlo approaches for epidemiology.

Key projects include Bayesian methods in machine learning and BigInsight Statistical and machine learning methods for sensor data. He serves as an advisor to PhD students like Aliaksandr Hubin and Ivar Grytten, with completed theses in graph-based genomics and Bayesian variable selection.

His work appears in journals like Transactions on Machine Learning Research, The Journal of Artificial Intelligence Research, and Journal of the Royal Statistical Society, covering topics from model sparsity to ecological forecasting.