New challenges are rising in the traditional statistical design and modeling for experimental data areas. When the experiments have complex structure, including quantitive and qualitative data type, or functional data output, new design and modeling methods for such complicated experiments are introduced. Computer experiment is another challenging area. Space-filling design and surrogate modeling methods are developed.
Lulu Kang is an Associate Professor at the Applied Math Department and her main research area is in statistics theories and methodologies. Kang also holds a Ph.D in Industrial Engineering and a M.S. in Operations Research.
Lunch will be provided for those that RSVP at least 24 hours prior to the seminar.