News Archive

2021/11: I am virtually attending the Deep Learning and Partial Differential Equations workshop as a part of the Mathematics of Deep Learning programme at the Isaac Newton Institute for Mathematical Sciences, Cambridge UK.

2021/10: I am virtually attending the Statistical Aspects of Non-Linear Inverse Problems workshop hosted by the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) as an invited participant from October 31st to November 5th.

2021/10: I am giving an invited talk at the Caltech CMX Student Seminar on some of my Ph.D. work on operator regression.

2021/09: My paper on Banach space random feature methods, joint work with A.M. Stuart, was published in the SIAM Journal on Scientific Computing.

2021/09: I am virtually attending the Deep Learning and Inverse Problems workshop as a part of the Mathematics of Deep Learning programme at the Isaac Newton Institute for Mathematical Sciences, Cambridge UK.

2021/08: My new preprint on "Convergence Rates for Learning Linear Operators from Noisy Data," joint with M.V. de Hoop, N.B. Kovachki, and A.M. Stuart, is now available. In it, we prove that a class of compact, bounded, and even unbounded operators can be stably estimated from noisy input-output pairs.

2021/07: I gave a SIAM AN21 talk on July 19th titled "Function Space Random Feature Methods for Learning Parametric PDE Solution Operators," with particular emphasis on fast learned surrogates for Bayesian inverse problems.

2021/06: I presented my forthcoming joint work on "Learning Unbounded Operators" to the Geo-Mathematical Imaging Group at Rice University on June 15th.

2021/04: I was admitted to candidacy for the Ph.D. degree.

2021/03: At SIAM CSE21, I co-organized (with Nathaniel Trask and Ravi Patel) the virtual minisymposiums "Learning Operators from Data" and "Machine Learning for Surrogate Model and Operator Discovery."

2021/01: I was invited to speak at the SIAM Annual Meeting (AN21) virtual minisymposium "Deep Learning for High-Dimensional Parametric PDEs" in July; looking forward to it!

2020/12: I participated in the virtual Workshop on Mathematical Machine Learning and Applications hosted by the CCMA at Penn State.

2020/11: I virtually gave an invited talk about my work on random feature methods for parametric PDEs in the numerical analysis and machine learning reading group seminar at the Courant Institute of Mathematical Sciences, New York University.

2020/09: I virtually gave an invited talk (both live and pre-recorded) in the Kernel Methods session of the Second Symposium on Machine Learning and Dynamical Systems at The Fields Institute, Toronto, Canada.

2020/07: I participated in the virtual Learning Models from Data: Model Reduction, System Identification and Machine Learning GAMM Juniors’ Summer School on Applied Mathematics and Mechanics at the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, where I presented a poster. I also attended the MSML2020 online conference at Princeton University.

2020/04: I virtually attended Workshop II: PDE and Inverse Problem Methods in Machine Learning at the IPAM High-Dimensional Hamilton-Jacobi PDEs long program at UCLA, Los Angeles, CA.

2020/02: I participated in the Inverse Problems: Algorithms, Analysis and Applications workshop at Caltech, through the CMX group in the CMS department.