With the start of the 2016-17 school year, faculty members in the Department of Statistics and Operations Research have received a number of new research grants from national research funds, to support their research in statistics and operations research with applications in biology, health care, data science and machine learning, and other areas.
Among the awardees, Professors Nilay Argon and Serhan Ziya have received an NSF collaborative research grant to study distribution of patients to medical facilities in mass casualty events. Professor Shankar Bhamidi has been award an NSF grant to study dynamic network models on entrance boundary and continuum scaling limits, condensation phenomena and probabilistic combinatorial optimization. Professor Yufeng Liu has received an NSF grant to organize the Conference on Statistical Machine Learning and Data Science, and an NSF collaborative research grant to do research on foundations of nonconvex problems in bigdata science and engineering on models, algorithms, and analysis.
Professors James Marron and Jan Hannig have been awarded a NSF grant to conduct research on statistical approaches to big data analytics. Professor Andrew Nobel has received an NSF grant to study random dynamical systems and limit theorems for optimal tracking, an NSF grant with Professor Shankar Bhamidi to study iterative testing procedures and high-dimensional scaling limits of extremal random structure, and an NIH grant with Professor Fred Wright to study multi-tissue and network models for next-generation EQTL. Professor Quoc Tran-Dinh has been awarded an NSF grant to study efficient methods for large scale self concordant convex minimization. Professor Yin Xia has received an NSF grant to do research on large-scale multiple testing for high-dimensional covariance structures with applications to genomics and neuroimaging. Professor Kai Zhang has received an NSF grant to study geometric perspectives on the correlation, and an NSF collaborative research grant for his research on statistical theory and methods beyond the dimensionality barrier.