Weng Kee Wong

Department of Biostatistics, Fielding School of Public Health, University of California at Los Angeles

Title:  Using Animal Instincts to Find Efficient Experimental Designs for Biomedical Studies


I discuss nature-inspired metaheuristic algorithms as general purpose tools for solving
optimization problems in statistics. The approach works quite magically and frequently
finds an optimal solution or a nearly optimal solution quickly. There is virtually no
explicit assumption required for such methods to be applicable and the user only
needs to input a few easy tuning parameters. I focus on one of the more popular
algorithms, particle swarm optimization (PSO), and demonstrate its ability to find
various types of optimal designs for biomedical studies, including optimal designs for
generalized linear models with many interacting factors and standardized maximin
optimal designs, where effective algorithms to find them have remained stubbornly
elusive until now.