
Contact Information
Mailing address: see http://ww1.math.nus.edu.sg/contactus.aspx
Office: National University of Singapore, S170815
Email: matyh at nus dot e d u dot s gPositions
Assistant Professor 2017
Department of Mathematics
National University of SingaporeVisiting Assistant Professor 20152017
Department of Mathematics
Duke UniversityEducation
Ph.D. in Mathematics 20122015
Stanford University
Advisor: Lexing Ying
M.s. in Mathematics 20102012
the University of Texas at Austin
B.s. in Mathematics 20062010
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 highdimensional 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 largescale scientific computing. Many challenges remain open and attract much attention especially in the highfrequency regime. This minisymposium focuses on recent advances in integral equations and integral transforms for highly oscillatory phenomena, including new formulations for highfrequency wave propagation, efficient and accurate discretizations, novel fast algorithms and their implementation based on locally rankstructured matrices and nonoscillatory phase functions, with applications in various imaging science and computational electromagnetism. Section 1, Section 2.
Organizing SIAM Conference on Imaging Science 2018 Minisymposium: LowDimensional Structures in Imaging Science
Many objects of interest in imaging science exhibit a lowdimensional structure, which could mean, for instance, low sparsity of a vector, lowrank property of a large matrix, or lowdimensional manifold model for a data set. Many successful methods rely on deep understanding and clever exploitation of such lowdimensional structures. The goal of this minisymposium is to bring together researchers actively working on imaging techniques based on lowdimensional models, and to explore some recent stateoftheart 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: LargeScale 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 largescale 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 lowrank structure of the matrix representations of these opertors and transforms. The butterfly algorithm or butterfly factorization is able to evaluate the matrixvector multiplication with nearly linear operation and memory complexity. This MATLAB package is available at Codes.