I am a second-year graduate student 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. Prior to joining Caltech, I received a MSci degree in Mathematics from Imperial College London.
My work lies at the intersection of data assimilation, stochastic analysis, dynamical systems, computational statistics and machine learning. I have a keen interest in research for the mathematical foundations of data science and using theoretical insight to develop novel computationally efficient algorithms.
I am an instructor and university ambassador for NVIDIA's Deep Learning Institute, for which I regularly organize workshops.
08/20/2023 I am co-organizing the mini-symposium "Data-driven Methods for Rough PDEs" at the International Congress on Industrial and Applied Mathematics in Tokyo, Japan.
02/28/2023 I co-organized the mini-symposium "Blending Learning and Dynamical Systems" at the SIAM Computational Science and Engineering Conference in Amsterdam, The Netherlands.
09/30/2022 I co-organized the mini-symposium "Frontiers in Monte Carlo Methods for Physics" at the SIAM Mathematics of Data Science Conference in San Diego, CA, USA.
09/27/2022 Our work "Ensemble Kalman Methods: A Mean Field Perspective" was released on ArXiV.