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Yonghan Jung is joining the School of Information Sciences at the University of Illinois Urbana-Champaign as an assistant professor on August 16, 2025. His research is centered on causal data science, focusing on creating and applying artificial intelligence and machine learning methodologies to reliably estimate causal effects from complex, real-world data. He works on multiple facets of causal inference, including the development of general universal estimation frameworks applicable to computing causal effects from intricate data-generating models, enhancing trustworthy AI methodologies by quantifying uncertainties using counterfactual analysis, and addressing causal challenges in complex data-generating processes. His expertise particularly lies in semiparametric causal effect estimation and debiased machine learning, with critical applications in areas like explainable AI and healthcare science. He has made significant scientific contributions by advancing the development of practical methods for estimating complex real-world effects and enhancing the understanding of the causal relationships within data.
GRE is optional for admission to all graduate programs in Statistics. Full status admission requires higher language scores than limited status.