Peter X. K. Song

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Dr. Song interested in the development and application of theories and methodologies from Data Science to solve scientific problems arising from medical and public health sciences, in particular from the fields of environmental health sciences and nutritional sciences. People from his lab are strongly interested in interdisciplinary research in the areas of statistics, operation research, and machine learning, with the core interest in the statistical foundation of big data analytics, and with target applications in processing and analyzing big data from various applied sciences, including asthma, environmental health sciences, nephrology, and nutritional sciences. His research projects have been funded by NIH, NSF and DARPA funding agencies. Visit Song Lab webpage for detail: http://www.umich.edu/~songlab/

Jon Lee

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Jon’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems.  On particular area of applied interest is environmental monitoring and the framework of maximum-entropy sampling.

Barzan Mozafari

Barzan Mozafari

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Building data-intensive systems that are more scalable, more robust, and more predictable. He draws from advanced statistical models to deliver practical database solutions to real-world problems. In particular, he adapts concepts and tools from applied statistics, optimization theory, and machine learning.

Xuming He

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Research interests include quantile regression modeling for associations related to possibly unusual or extreme events, subgroup analysis, and uncertainty quantification after model selection.