- About Us
- Affiliates and Liaisons
Computational Statistics, especially problems that involve numerical optimization, e.g., the development of tractable formulations of and efficient numerical algorithms for multidimensional scaling and other methods for embedding dissimilarity data.
Statistical Learning, i.e., multivariate data-analytic techniques for nonlinear dimension reduction (manifold learning), classification, and clustering. Current interests include the application of distance geometry to the problem of inferring 3-dimensional molecular structure from distance restraints, and various high-dimensional classification problems in bioinformatics.
Design & Analysis of Computer Experiments, specifically for the purpose of optimizing computationally expensive computer simulations. Current interests include the application of statistical decision theory to computer-assisted robust design.
Stochastic Optimization and Response Surface Methodology, especially for tuning the inputs of highly nonlinear stochastic simulations and estimating the parameters of analytically intractable stochastic processes. Current interests include developing quasi-Newton methods for optimization in the presence of random noise.
Bold student names indicate a cognitive science standalone student.
|Blaha, Leslie||A Dynamic Hebbian-style Model of Configural Learning (December 2010)||Townsend, J. (Co-Chair), Busey, T. (Co-Chair), Gold, J,. Trosset, M.|