World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
35
Citations
5834
World Ranking
2755
National Ranking
1128

Research.com Recognitions

  • 2020 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2013 - Fellow of the American Mathematical Society
  • 2003 - Fellow of John Simon Guggenheim Memorial Foundation

Overview

Jack Xin is a researcher affiliated with the University of California, Irvine in the United States. Their academic work spans primarily across the fields of Computer Science and Mathematics, with a significant focus on specialized subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Modeling and Simulation, and Statistical and Nonlinear Physics.

Their research covers a variety of topics, notable among them are Sparse and Compressive Sensing Techniques, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Mathematical Biology related to Tumor Growth, Medical Image Segmentation Techniques, Model Reduction and Neural Networks, and Advanced Mathematical Modeling in Engineering.

Recent publications by Jack Xin demonstrate a diverse range of contributions. Noteworthy papers include:

  • Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization (2021) published in Frontiers in Applied Mathematics and Statistics
  • A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford--Shah Color and Multiphase Image Segmentation (2021) in SIAM Journal on Imaging Sciences
  • Fourier-Mixed Window Attention: Accelerating Informer for Long Sequence Time-Series Forecasting (2023) published on arXiv (Cornell University)
  • Spectral analysis and computation for homogenization of advection diffusion processes in steady flows (2020) in Journal of Mathematical Physics
  • A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19 (2020) on bioRxiv (Cold Spring Harbor Laboratory)

Frequent collaborators in Jack Xin's work include Zhiwen Zhang, Zhongjian Wang, Kevin Bui, Fredrick Park, and Yingyong Qi.

Their publications have appeared repeatedly in key venues such as arXiv (Cornell University), IEEE Access, SSRN Electronic Journal, Frontiers in Applied Mathematics and Statistics, and Multiscale Modeling and Simulation.

Jack Xin has been recognized as a Fellow of the American Association for the Advancement of Science (AAAS) since 2020, a Fellow of the American Mathematical Society from 2013, and a Fellow of the John Simon Guggenheim Memorial Foundation since 2003.

Best Publications

  • Front Propagation in Heterogeneous Media

    Jack Xin

  • Minimization of $ll_{1-2}$ for Compressed Sensing

    Penghang Yin;Yifei Lou;Q. I. He;Jack Xin

  • A Convex Model for Nonnegative Matrix Factorization and Dimensionality Reduction on Physical Space

    E. Esser;M. Moller;S. Osher;G. Sapiro

  • A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing

    Yifei Lou;Tieyong Zeng;Stanley J. Osher;Jack Xin

  • A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications

    Ernie Esser;Yifei Lou;Jack Xin

  • Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of L 1 and L 2

    Yifei Lou;Penghang Yin;Qi He;Jack Xin

  • Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

    Penghang Yin;Jiancheng Lyu;Shuai Zhang;Stanley J. Osher

  • Existence of planar flame fronts in convective-diffusive periodic media

    Jack X. Xin

  • Minimization of Transformed $L_1$ Penalty: Theory, Difference of Convex Function Algorithm, and Robust Application in Compressed Sensing

    Shuai Zhang;Jack Xin

  • Existence and nonexistence of traveling waves and reaction-diffusion front propagation in periodic media

    Jack X. Xin

  • Existence of KPP fronts in spatially-temporally periodic advection and variational principle for propagation speeds

    James Nolen;Matthew Rudd;Jack Xin

  • An Introduction to Fronts in Random Media

    Jack Xin

  • Multidimensional Stability of Traveling Waves in a Bistable Reaction–Diffusion Equation, I

    J.X. Xin

  • On the Incompressible Fluid Limit and the Vortex Motion Law of the Nonlinear Schrödinger Equation

    F.-H. Lin;J. X. Xin

  • A convex model for non-negative matrix factorization and dimensionality reduction on physical space

    Ernie Esser;Michael Möller;Stanley Osher;Guillermo Sapiro

  • Existence of KPP type fronts in space-time periodic shear flows and a study of minimal speeds based on variational principle

    James Nolen;Jack Xin

  • Ratio and difference of $l_1$ and $l_2$ norms and sparse representation with coherent dictionaries

    Penghang Yin;Ernie Esser;Jack Xin

  • Modeling light bullets with the two-dimensional sine—Gordon equation

    J. X. Xin

  • Difference-of-Convex Learning: Directional Stationarity, Optimality, and Sparsity

    Miju Ahn;Jong-Shi Pang;Jack Xin

  • Deep Learning for Real-Time Crime Forecasting and Its Ternarization

    Bao Wang;Penghang Yin;Andrea Louise Bertozzi;P. Jeffrey Brantingham

  • BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights

    Penghang Yin;Shuai Zhang;Jiancheng Lyu;Stanley J. Osher

Frequent Co-Authors

Yifei Lou
Yifei Lou University of North Carolina at Chapel Hill
Stanley Osher
Stanley Osher University of California, Los Angeles
Gregory L. Eyink
Gregory L. Eyink Johns Hopkins University
Andrea L. Bertozzi
Andrea L. Bertozzi University of California, Los Angeles
Fanghua Lin
Fanghua Lin Courant Institute of Mathematical Sciences
Guillermo Sapiro
Guillermo Sapiro Princeton University
Hongkai Zhao
Hongkai Zhao Duke University
Fan-Gang Zeng
Fan-Gang Zeng University of California, Irvine
Anthony Peirce
Anthony Peirce University of British Columbia
Jerome V. Moloney
Jerome V. Moloney University of Arizona

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in Mathematics, exploring related online degrees can open diverse career opportunities. Many professionals complement mathematical skills with business knowledge through degrees like the easy online MBA, which offers flexibility and foundational management training.

For those aiming for advanced leadership roles in academia or industry, pursuing a Doctor of Business Administration is viable. The cheapest DBA online programs provide cost-effective options without compromising quality.

Many math graduates also flourish in finance. Earning a master of finance online can sharpen analytical skills relevant to financial modeling, risk assessment, and investment strategies.

Time is often crucial, so accelerated paths like accelerated MBA programs online help candidates quickly gain qualifications and advance their careers, blending quantitative expertise with leadership acumen.

Best Scientists Citing Jack Xin

Trending Scientists