Mona's research focuses on developing new methodologies to understand and quantify the dependency structure in data. Finding interpretable measures of the degree of dependence between the variables is a fundamental task in Statistics. Such measures are the core ingredient of many areas, such as variable selection, dimensionality reduction, sensitivity analysis, and causal inference.
Mona is also working on a generalization of this problem, measuring conditional dependence and the hypothesis testing problem of conditional independence with applications in causal inference and graphical models. She is also interested in non-parametric statistics and problems in high-dimension.
Before joining LSE, Mona earned her Ph.D. in Statistics at Stanford University in 2020 and was an FDS Postdoc Fellow at Seminar for Statistics at ETH Zürich.