Daniel Sanz-Alonso

Daniel Sanz-Alonso is an Associate Professor in the Department of Statistics at the University of Chicago and a member of the Committee on Computational and Applied Mathematics. Prior to this, he was a postdoc at Brown University's Division of Applied Mathematics and Data Science Initiative (2016-2018), following a PhD in Mathematics and Statistics (2016) at the University of Warwick under Andrew Stuart and Gareth Roberts.

His research focuses on inverse problems, data assimilation, and scientific machine learning, aiming to integrate complex predictive models with large datasets through theoretical and computational frameworks. His work intersects data science, machine learning, partial differential equations, and calculus of variations, with applications in weather forecasting, geophysical sciences, and machine learning.

Scientific Awards

• José Luis Rubio de Francia Prize (2020) – Recognized as the best Spanish mathematician under 32 by the Spanish Royal Society of Mathematics
• NSF CAREER award (2023)

He has received research support from the National Science Foundation, National Geospatial-Intelligence Agency, Department of Energy, and BBVA Foundation (€35,000 grant over three years). Since 2025, he serves as Associate Editor for the SIAM/ASA Journal on Uncertainty Quantification.