Image
Position Title/Affiliation
University of Texas at Austin
Position Being Sought 2023
Bayesian Computation Program Chair
Candidate Statement 2023
I am excited to run for the role of Bayesian Computation Program Chair. I am currently an Associate Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. My commitment to the ISBA community is reflected in my contributions as a past member of the ISBA Nominating Committee, as an instructor at the last two biannual ISBA meetings, and my organizing of several sessions at ISBA events.
If elected, my vision is to foster an inclusive environment where Bayesian computation is viewed from a holistic perspective, emphasizing practical computational tools aimed at specific applications. A point of emphasis will be to organize exciting online events to promote BayesComp. I also have a commitment to diversity and inclusivity at UT Austin, and I aim to further enhance the diversity and accessibility of our events.
My research focuses on developing Bayesian Nonparametric methodology, with a particular interest in Bayesian decision tree methods as they apply to causal inference. I have also worked on fundamental/foundational problems presented by Bayesian causal inference and missing data problems. My passion for translating theory into practice has led me to develop advanced MCMC schemes to fit models for both tree-valued parameters and generic discrete spaces, while maintaining an open mind to alternate approaches to posterior exploration.
For more details about my work and contributions, I have a personal website at theodds.github.io, which also includes a link to my Google Scholar profile.
If elected, my vision is to foster an inclusive environment where Bayesian computation is viewed from a holistic perspective, emphasizing practical computational tools aimed at specific applications. A point of emphasis will be to organize exciting online events to promote BayesComp. I also have a commitment to diversity and inclusivity at UT Austin, and I aim to further enhance the diversity and accessibility of our events.
My research focuses on developing Bayesian Nonparametric methodology, with a particular interest in Bayesian decision tree methods as they apply to causal inference. I have also worked on fundamental/foundational problems presented by Bayesian causal inference and missing data problems. My passion for translating theory into practice has led me to develop advanced MCMC schemes to fit models for both tree-valued parameters and generic discrete spaces, while maintaining an open mind to alternate approaches to posterior exploration.
For more details about my work and contributions, I have a personal website at theodds.github.io, which also includes a link to my Google Scholar profile.