Michael Trosset: Cognitive Science Program: Indiana University Bloomington
Field of study
- Computational statistics, statistical learning, multidimensional scaling, nonlinear dimension reduction, classification, clustering
Education
- Ph.D., University of California, Los Angeles, 1989
Research interests
- 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.