The National Science Foundation (NSF) awarded a $100,000 project grant to The Trustees of Columbia University in the City of New York under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The grant supports collaborative research to advance nonparametric learning methods for high-dimensional survival data analysis, with applications in causal inference and sequential decision making. Key objectives include developing supervised dimension reduction techniques, robust nonparametric frameworks for estimating causal treatment effects, and multi-stage algorithms for personalized service in high-dimensional censored bandit problems. This research aims to provide novel analytical tools beneficial to medical researchers, financial institutions, and other practitioners dealing with complex, high-dimensional data with survival outcomes. The project will also create valuable training and research opportunities for graduate and undergraduate students, including those from underrepresented minority groups.
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