Assistant professor,
Department of Math, UC Irvine.
Office: Rowland Hall 540J
E-mail: angxiun at uci dot edu
Name pronounces “ang sh-you knee”
Chinese name: 倪昂修
Recent
My adjoint path-kernel method solves a difficult variational data assimilation problem.
The first paper on fast response, released five years ago, is accepted by ARMA.
I gave a divergence-kernel formula for scores and linear responses of random, and gave a new framework for parametric SDE generative models.
Brief description of Research
I compute the derivatives of marginal or stationary distributions of random dynamical systems, which is typically chaotic / high-dimensional / small-noise. Conventionality, there are three basic methods: the path-perturbation method, the divergence method, and the kernel-differentiation method. I gave all combinations of two (out of three) basic methods, thus overcoming some major shortcomings of each basic method. More specifically, I gave
4. Divergence-kernel method for scores, linear responses, and parametric (in both drift and diffusion) SDE generative models.
3. Path-Kernel method for linear responses. This solve a difficult variational data assimilation problem.
2. Ergodic and foliated kernel differentiation (or likelihood ratio) method.
1. Path-divergence formula (also called the fast response formula), which is the pointwise expression for linear responses of hyperbolic deterministic chaos. This builds on several of my previous results, such as the equivariant divergence formula, the adjoint shadowing lemma, and the nonintrusive shadowing algorithm.
I am also interested in dynamical system and probability and their interaction with all fields, such as fluids, geophysics, inference, data assimilation, and machine learning.
Misc.
Curriculum vitae
I got my PhD from UC Berkeley math, did a postdoc at PKU BICMR, then worked as Assistant professor at YMSC and Qiuzhen College in Tsinghua University.
My mentors: John Strain, Pingwen Zhang, Mark Pollicott, Jack Xin, Qing Nie, Long Chen.
Yi’s Webpage.