I'm a postdoctoral associate at the Center for Applied Mathematics at Cornell University, working with David Matteson and Gennady Samorodnitsky.
I received my PhD from the Department of Statistical Science at Duke University where I was advised by Peter Hoff and David Dunson.
Very broadly, I'm interested in methodology for multivariate data. My dissertation research addresses challenges which arise in Bayesian analyses of statistical models with orthogonal matrix parameters, including computation and prior specification.
My CV can be found here. I can be reached by email at firstname.lastname@example.org.
Publications and preprints
- Michael Jauch, Peter D. Hoff, and David B. Dunson. Monte Carlo simulation on the Stiefel manifold via polar expansion. Submitted, 2019. [arxiv] [code]
- Michael Jauch, Peter D. Hoff, and David B. Dunson. Random orthogonal matrices and the Cayley transform. Submitted, 2018. [arxiv] [code]
- Michael Jauch, Paolo Giordani, and David B. Dunson. A Bayesian oblique factor model with extension to tensor data. Proceedings of the Conference of the Italian Statistical Society, 2017.
- Michael Jauch and Victor Peña. Bayesian optimization with shape constraints. NIPS Workshop on Bayesian Optimization, 2016. [arxiv]
Talks and posters
- Contributed poster at the Joint Statistical Meetings in Denver. July 2019.
- Invited talk at the Statistics Seminar at Cornell. March 2019.
- Contributed talk at the Joint Statistical Meetings in Vancouver. August 2018.
- Invited talk at the 2018 ISBA World Meeting in Edinburgh. June 2018.
- Contributed poster at the Joint Statistical Meetings in Baltimore. August 2017.
- Invited talk at the Conference of the Italian Statistical Society in Florence. June 2017.
- Contributed poster at the NIPS Workshop on Bayesian Optimization in Barcelona. December 2016.
- Contributed poster at the 2016 ISBA World Meeting in Sardinia. June 2016.