Research

My research focuses on using statistical methodology to understand complex real-life problems, with an emphasis on Bayesian inference. While most of my work is in climate, I am also interested in working on any interesting problems where my statistical expertise can be useful.

Research themes

• Long-range dependence

  Approximating long memory as a mixture of four AR(1) processes, for fast Bayesian inference on climate records.

• Age-depth modeling

  Dating uncertainty and synchronization of layer-counted paleoclimate proxy records.

• Early warning signals

  A Bayesian model-based approach for identifying early warning signals preceding tipping points.

I have also worked on other topics, including deep learning methods as well as data integration. This page will be updated as more papers get published. Papers from all of these areas are listed under Publications.

If you are interested in a collaboration, please feel free to contact me.