• Contact Information

    Mailing address: see http://ww1.math.nus.edu.sg/contactus.aspx
    Office: National University of Singapore, S17-08-15
    Email: matyh at nus dot e d u dot s g


    Assistant Professor 2017-
    Department of Mathematics
    National University of Singapore

    Visiting Assistant Professor 2015-2017
    Department of Mathematics
    Duke University


    Ph.D. in Mathematics 2012-2015
    Stanford University
    Advisor: Lexing Ying

    M.s. in Mathematics 2010-2012
    the University of Texas at Austin

    B.s. in Mathematics2006-2010
    Shanghai Jiao Tong University, China
    Advisor: Zhenli Xu

Latest news

Organizing 9th International Congress on Industrial and Applied Mathematics - ICIAM 2019 Minisymposium: Mathematical Theory and Applications of Deep Learning

The “unreasonable effectiveness” of deep learning for massive datasets posed numerous mathematical and algorithmic challenges along the path towards gaining deeper understandings of new phenomena in machine learning. This minisymposium aims at bringing together applied mathematicians interested in the mathematical aspects of deep learning, with diverse background and expertise ranging from approximation theory, optimization methods, and generalization performance to modeling high-dimensional scientific computing problems and nonlinear physical systems; the talks reflect the collaborative, multifaceted nature of the mathematical theory and applications of deep neural networks. Section 1 concerns the approximation capacity and optimization of deep learning, Section 2 concerns the generalization and perturbation error of deep learning, Section 3 concerns the applications of deep learning.

Organizing SIAM Conference on Computational Science and Engineering 2019 Minisymposium: Fast and Accurate Integral Methods for Highly Oscillatory Phenomena

Integral methods are useful tools in applied science and engineering. In particular, they are an important topic for large-scale scientific computing. Many challenges remain open and attract much attention especially in the high-frequency regime. This minisymposium focuses on recent advances in integral equations and integral transforms for highly oscillatory phenomena, including new formulations for high-frequency wave propagation, efficient and accurate discretizations, novel fast algorithms and their implementation based on locally rank-structured matrices and non-oscillatory phase functions, with applications in various imaging science and computational electromagnetism. Section 1, Section 2.

Organizing SIAM Conference on Imaging Science 2018 Minisymposium: Low-Dimensional Structures in Imaging Science

Many objects of interest in imaging science exhibit a low-dimensional structure, which could mean, for instance, low sparsity of a vector, low-rank property of a large matrix, or low-dimensional manifold model for a data set. Many successful methods rely on deep understanding and clever exploitation of such low-dimensional structures. The goal of this mini-symposium is to bring together researchers actively working on imaging techniques based on low-dimensional models, and to explore some recent state-of-the-art work in scientific computation, machine learning and optimization related with imaging science. Section 1, Section 2, Section 3.

Organizing SIAM Conference on Applied Linear Algebra 2018 Minisymposium: Large-Scale Eigenvalue Problems and Applications

Eigenvalue problem is the essential part and the computationally intensive part in many applications in a variety of areas, including, electron structure calculation, dynamic systems, machine learning, etc. In all these areas, efficient algorithms for solving large-scale eigenvalue problems are demanding. Recently many novel scalable eigensolvers were developed to meet this demand. The choice of an eigensolver highly depends on the properties and structure of the application. This minisymposium in- vites eigensolver developers to discuss the applicability and performance of their new solvers. The ultimate goal is to assist computational specialists with the proper choice of eigensolvers for their applications. Section 1, Section 2.

A MATLAB package ButterflyLab

ButterflyLab is a MATLAB toolbox for fast evaluation of multidimensional Fourier integral operators for wave equations and a class of transforms in harmonic analysis. Algorithms in this package are based on the comprementary low-rank structure of the matrix representations of these opertors and transforms. The butterfly algorithm or butterfly factorization is able to evaluate the matrix-vector multiplication with nearly linear operation and memory complexity. This MATLAB package is available at Codes.

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