AMS Seminar: Ann Lee (Carnegie Mellon University) @ Whitehead 304

Title: Uncertainty Quantification and Nonparametric Inference for Complex Data and Simulations
Abstract:  Recent technological advances have led to a rapid growth in not just the amount of scientific data but also their complexity and richness. Simulation models have, at the same time, become increasingly detailed and better at capturing the underlying processes that generate observable data. On the statistical methods front, however, we still lack tools that accurately quantify complex relationships between data and model parameters, as well as adequate tools to validate models of multivariate likelihoods and posteriors. In this talk, I will discuss our current work on addressing some of the multi-faceted challenges encountered in astronomy but more generally applicable to fields involving massive amounts of complex data and simulations; in particularly, challenges related to (i) building conditional probability models that can handle inputs of different modalities, e.g. photometric data and correlation functions, (ii) estimating non-Gaussian likelihoods and posteriors via simulations, and (iii) assessing the performance of complex models and simulations when the true distributions are not known. I will draw examples from photometric redshift estimation and from the inference of cosmological parameters. (Part of this work is joint with Rafael Izbicki, Taylor Pospisil, Peter Freeman, Ilmun Kim, and the LSST-DESC PZ working group)