Josh Griffin received his PhD in Mathematics from the University of California, San Diego in 2005. As a Senior Manager in the Advanced Analytics Department, Josh leads a R&D team in SAS/OR devoted to developing software for general and special purpose nonlinear optimization with a current focus on big data problems arising in the context of machine learning. Prior to joining SAS, Josh was a senior member of the technical staff at Sandia National Laboratories developing derivative-free software for simulation based optimization in a distributed environment.
Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models
