The Department seeks candidates pursuing research in one or more areas central to modern Data Science, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems, or data assimilation, who have the potential to supervise PhD and master''s student research with ability to contribute to curriculum needs in the department. The concentration integrates departmental strengths across Statistics (e.g., statistical learning, causal inference, experimental design), theoretical areas (e.g., probability, stochastic PDEs, algebraic statistics, topological data analysis), and computational areas (e.g., scientific machine learning, optimization, inverse problems, data assimilation).