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I am a fifth-year PhD candidate in Applied and Computational Mathematics in the Department of Computing and Mathematical Sciences at Caltech, where I am grateful to be advised by Prof. Andrew Stuart and supported by the Kortschak Scholars Program. From February to December 2025, I worked with the Learning and Perception Research group at NVIDIA as a research intern. Prior to joining Caltech, I received a MSci degree in Mathematics from Imperial College London.
My work lies at the intersection of data assimilation, mathematical analysis and machine learning. I have a keen interest in research for the mathematical foundations of the data sciences and using theoretical insight to develop novel computationally efficient algorithms for probabilistic inference.
Recent News
Recent News
Checkout our new preprint "Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting".
Our work "Learning Enhanced Ensemble Filters", joint with Eviatar Bach, Ricardo Baptista, Bohan Chen and Andrew Stuart was published in the Journal of Computational Physics.
Checkout our new preprint "Operator Learning at Machine Precision".
07/31/2025 With Ricardo Baptista and Nikola Kovachki I am organizing MS30 "Probabilistic Learning Methods for Inverse Problems" in the Applied Inverse Problems conference in Rio de Janeiro, Brazil.
07/29/2025 I will be speaking in MS03 "Data Assimilation for Inverse Problems" in the Applied Inverse Problems conference in Rio de Janeiro, Brazil.
Our paper "Ensemble Kalman methods: a mean-field perspective", joint work with Sebastian Reich and Andrew Stuart was published in Acta Numerica, 2025.
Checkout our new preprint "Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks": joint work with Ricardo Baptista, Matthieu Darcy, Houman Owhadi, Andrew Stuart and Xianjin Yang.
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