Tracy Holsclaw
Email: tholscla at soe.ucsc.edu Home Research Teaching Education Biography	Research Published Papers Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets, to appear in Physical Review D, The American Physical Society, (2011) Nonparametric Dark Energy Reconstruction from Supernova Data Physical Review Letters, 105, 241302, The American Physical Society, (2010) Featured in: Physorg.com news article entitled "Statistical modeling could help us understand cosmic acceleration" Featured in: Jack Baskin School of Engineering news article entitled "Statistical analysis to explore the evolution of the universe" Nonparametric Reconstruction of the Dark Energy Equation of State Physical Review D, 82, 103502, The American Physical Society, (2010) Submitted Papers Gaussian Process Modeling of Derivative Curves (submitted for publication 8/2011) Team Projects Translating Differential Equation Models Into Kernel Methods for Data Analysis - Phase III (NASA/SJSU) (Large file) Mathematical and Statistical Analysis of Heat Pipe Design (Intel/SJSU) Statistical Analysis of a Non-Linear Chemical Equation Using Numerical Methods (NASA/SJSU) Posters Presenstations Dissertation - research done under the advisement of statisticians, Herbie Lee (UCSC) and Bruno Sanso (UCSC). The body of work covers modeling derivative curves with Gaussian processes. This work required extensive research and development of Gaussian process methods applied to inverse problems. A cosmology application drove this statistical research to model dark energy equation of state. The dissertation was a results of an ISSDM collaboration with Los Alamos National Labs cosmologists Katrin Heitmann, Salman Habib and Ujjaini Alam and statistician David Higdon. We worked with Bayesian parametric and non-parametric models to fit non-linear equations to supernova, baryon acoustic oscilliation, and cosmic microwave backgound radiation data to elicit the form of dark energy. We have exploited the stochastic properties of the Gaussian process to perform analysis for this inverse applied problem.