Maasoumi is considered by some as an intellectual leader of the Empirical/Statistical “Income Inequality” literature, with influential works on multidimensional well-being, mobility, and poverty. His measures of aggregation, poverty and inequality in many dimensions are standards in the field, as is his leading work on stochastic dominance (with Oliver Linton and others).
He has also been a known leader and innovator in the field of Information Theory, where he is regarded as a creative force in conceptualization of econometric and economic objects with information theory criteria and techniques first developed in communication theory.
His areas of research and interest range widely. They include modern Machine Learning, deep learning, automated debiased partial effects, financial econometrics, nonlinear time series models and methods, Information Theory, Aggregation, Econometric tests and estimators, finite sample distribution theory, forecasting, empirical finance, Patent infringement methods, non-parametric methods, and the aforementioned areas of inequality, poverty, and mobility, especially in many dimensions. His work on program evaluation and policy analysis joins innovations in information theory, stochastic dominance testing, and treatment effect analysis.