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:

Talk on multi-scale Bayesian transport maps, a scalable Bayesian emulator for nonstationary spatial fields that models multiple resolutions jointly through autoregressive transport maps, applied to climate model downscaling with full posterior uncertainty quantification.