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I am an assistant professor in the Mathematical-Statistical Method group at Wageningen University since June 2019. My research focuses on the development of statistical methods for time-series data, dynamic networks, and graphical models. I am also interested in causal discovery and prediction to improve the understanding of complex systems and processes. As applications of my research, I focus on plant sciences, genetics, computational biology and food and nutrition.
Other position: Member of the Wagenigen Young Academy
Prior to my tenure track appointment at WUR, I was a Post-doc researcher between June 2017 and June 2019 in the Applied Mathematical-Statistical group at WU. During my Post-doc, I worked on a project entitled “Network-based statistical inference for studying muscle health in relation to physical functioning and nutrition". My research there involved methodological and software developments. I was involved also in other multi-disciplinary projects.
Prior to joining the group at WUR, I was a PhD candidate at the University of Groningen. During my PhD, I have developed statistical methodologies and software packages based on probabilistic graphical models for high-dimensional data. This has resulted in my PhD thesis entitled “Extensions of Graphical Models with Applications in Genetics and Genomics”. I have defended my thesis in January 2018. I have an M.Sc. degree in Mathematical Statistics and B.Sc degree in Statistics.
Science award: An international jury awarded the Hans van Houwelingen 2020 award for the best Dutch biometry paper of 2018 and 2019 to Pariya Behrouzi (Wageningen University) for her paper with Ernst Wit, titled `Detecting epistatic selection with partially observed genotype data by using copula graphical models' published in JRSS-C in 2019.
Software: I am the (co-) developer of the following open-source software which are used in my publications:
- netgwas R-package: Network-Based Genome Wide Association Studies
- netShiny R-package: Tool for Comparison and Visualization of Multiple Networks
- tsnetwork R-package: Dynamic Chain Graph Models for Ordinal Time Series Data
- nutriNetwork R-package: Structure Learning with Copula Graphical Model