Maseeh Mathematics and Statistics Colloquium: Towards large-scale data assimilation with structured generative models

Speaker: Ricardo Baptista, PhD
www.ricardobaptista.com
California Institute of Technology

Title: Towards large-scale data assimilation with structured generative models

Abstract: Accurate state estimation, also known as data assimilation, is essential for geophysical forecasts, ranging from numerical weather prediction to long-term climate studies. While ensemble Kalman methods are widely adopted for this task in high dimensions, these methods are inconsistent at capturing the true uncertainty in non-Gaussian settings. In this presentation, I will introduce a scalable framework for consistent data assimilation. First, I will demonstrate how inference methods based on conditional generative models generalize ensemble Kalman methods and correctly characterize the probability distributions in nonlinear filtering problems. Second, I will present a dimension reduction approach for limited data settings by identifying and encoding low-dimensional structure in generative models with guarantees on the approximation error. The benefits of this framework will be showcased in applications from fluid mechanics with chaotic dynamics, where classic methods are unstable in small sample regimes.

Biography: Dr. Baptista is an incoming Assistant Professor at the University of Toronto in the Department of Statistical Sciences and is currently a visitor at Caltech. Ricardo received his PhD from MIT, where he was a member of the Uncertainty Quantification group. The core focus of his work is on developing the methodological foundations of probabilistic modeling and inference. He is broadly interested in using machine learning methods to better understand and improve the accuracy of generative models for applications in science and engineering.

Faculty Host: Dr. Safa Mote