Department of Statistics and the College
Daniel Sanz-Alonso’s academic interests include data assimilation, inverse problems, machine learning, Monte Carlo methods, and uncertainty quantification. His research has been published in the SIAM (Society for Industrial and Applied Mathematics)/ASA (American Statistical Association) Journal on Uncertainty Quantification, the SIAM Journal on Mathematical Analysis, Communications in Mathematical Sciences, Inverse Problems, Statistical Science, and Physica D: Nonlinear Phenomena.
Sanz-Alonso completed a licenciatura degree in mathematics from the University of Valladolid in Spain, followed by a PhD in mathematics and statistics from the University of Warwick in the United Kingdom. Most recently, he was a postdoctoral research associate in the Division of Applied Mathematics at Brown University and a member of its Data Science Initiative.