Studies

 

Tracy Holsclaw

Email:  tholscla at sign ucsc dot edu 

I am PhD student working under Herbie Lee (UCSC) and Bruno Sanso (UCSC) on Statistical Modeling for Dark Energy. This is an ISSDM collaboration with Los Alamos National Labs cosmologists Katrin Heitmann, Salman Habib and Ujjaini Alam and statistician, Dave Higdon. We currently are working with Bayesian parametric and non-parametric models to fit non-linear equations to supernovae data to elicit the form of the equation of state for the posited dark energy. 

Posters

• Cosmic Calibration - Statistical Modeling for Dark Energy III
• Cosmic Calibration - Statistical Modeling for Dark Energy II
• Cosmic Calibration - Statistical Modeling for Dark Energy I

Presentations

• Cosmic Calibration - Statistical Modeling for Dark Energy

Education

• BS - Applied Mathematics (emphasis in Statistics) - San Jose State University
• MS - Mathematics - San Jose State University - thesis Investigation of Repeated Measures Linear Regression Methodologies

Courses I found interesting

• UCSC - Bayesian Modeling, Generalized Linear Models, Bayesian non-parametrics, Decision theory, Spatial statistics, Probability theory, and special topics in MCMC.
• SJSU - Regression Analysis, Survival analysis, and Partial differential equations.
• Future courses - Time series, Stochastic processes

Other team projects

• Translating Differential Equation Models Into Kernel Methods for Data Analysis - Phase III (Large file)
• Mathematical and statistical analysis of heat pipe design
• Statistical analysis of a nonlinear chemical equation using numerical methods

 

COURSES I have taken

Statistics:

UCSC - 205 Mathematical Statistics, 206 Bayesian Statistics, 207 Intermediate Bayesian Statistical Modeling, 221 Bayesian Decision Theory, 241 Bayesian Nonparametric Methods, 245 Spatial Statistics, 261 Probability Theory with Markov Chains, 274 Generalized Linear Models, ...

SJSU -  Linear Models and Survival Analysis

Applied Math: 

UCSC- 211 Foundations of Applied Mathematics 

SJSU - 233A Partial Differential Equations

Undergrad Background courses:

Intro to Stats, intro to probability, linear regression, mathematical statistics, ODE, PDE, linear algebra (one year), analysis (one year), C++ (one year), Matlab, S+, R, JmpIn, SPSS