Event Detail

Event Type: 
Applied Mathematics and Computation Seminar
Friday, April 21, 2017 -
12:00 to 13:00
GLK 113

Speaker Info


In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize computational model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and our approach as well as give example applications.

We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.