Alejandro Calle-Saldarriaga

Alejandro Calle-Saldarriaga

5th year Statistics PhD candidate working at the interface of Bayesian modeling and deep learning for large-scale scientific data: scalable Gaussian processes, deep generative models, and uncertainty quantification, mostly for climate and atmospheric problems. Graduating in 2027 and looking for jobs.

Generative multi-scale modeling via spatial autoregressive transport maps

Date:

Poster on multi-scale Bayesian transport maps: a scalable generative model that learns the joint non-Gaussian distribution of nonstationary spatial fields across resolutions, with applications to downscaling of climate model output.