Biography
Jack Xin is a Chancellor’s Professor in the Department of Mathematics at the University of California, Irvine. He completed his Ph.D. in Mathematics at the Courant Institute of New York University in 1990, after earning his B.S. in Mathematics from Peking University in 1985. Before joining UC Irvine in 2005, Xin held faculty positions at the University of Texas at Austin and the University of Arizona. He has been a Chancellor’s Professor at UC Irvine since 2019.
Xin's research focuses on analysis and computational methods with applications in physical and data sciences. He has published extensively in journals such as the SIAM Journal on Mathematical Analysis and the Journal of Computational Physics. His research has been continuously funded by the National Science Foundation (NSF), with grants supporting projects on algorithms and mathematical studies for applications in deep neural networks, among others. Xin has received multiple recognitions, including being named a Fellow of the American Association for the Advancement of Science in 2020 and a Fellow of the Society for Industrial and Applied Mathematics in 2021.
At UC Irvine, Xin teaches both undergraduate and graduate courses in applied mathematics. He has supervised numerous doctoral students who have gone on to careers in academia and industry. Xin has also contributed to the mathematical community through various service roles, such as serving on editorial boards for journals like Multiscale Modeling and Simulation and organizing conferences to promote advances in applied mathematics. Additionally, he has been actively involved in educational outreach programs, such as directing iCAMP to mentor undergraduate students in computational mathematics.
Return to topDegrees
- PhD in Mathematics, Courant Institute, New York University, 1990
- MS in Mathematics, Courant Institute, New York University, 1988
- BS in Mathematics, Peking University, 1985
Distinctions
- Fellow of Asia-Pacific Artificial Intelligence Association, 2024
- Qualcomm Gift Award, 2023-2024
- Qualcomm Faculty Award, 2018-2020, 2022
- Fellow of the Society for Industrial and Applied Mathematics, 2021
- Fellow of the American Association for the Advancement of Science, 2020
Areas of Expertise
- Mathematics and Computational Methods
- Deep Learning Algorithms
- Reaction-Diffusion Systems
- Applied Mathematics in Physical Sciences
- Nonlinear Dynamics and Waves
- Data Science and Signal Processing
- Machine Learning Applications
- Graph Neural Networks
- Turbulent Combustion Modeling
- Statistical Learning and Optimization
Recent Publications
- H. Gao, Z. Long, J. Xin, Y. Yu, “Existence of an effective burning velocity in a cellular flow for the curvature G-equation proved using a game analysis”, Journal of Geometric Analysis, vol. 34, no. 3, pp. 81, 2024.
- S-H. Wang, Y-C. Hsu, J. Baker, A. L. Bertozzi, J. Xin, B. Wang, “Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks”, 2024. Presented at International Conference on Learning Representations (ICLR), May, 2024.
- Z. Wang, J. Xin, Z. Zhang, “A DeepParticle method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems”, Physica D, vol. 460, pp. 134082, 2024.
- H. Mitake, C. Mooney, H. Tran, J. Xin, Y. Yu, “Bifurcation of homogenization and nonhomogenization of the curvature G-equation with shear flows”, Mathematische Annalen, 2024.
- K. Bui, Y. Lou, F. Park, J. Xin, “An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation”, Comm. on Applied Math and Computation, vol. 34, no. 3, 2024.
- J. Xin, Y. Yu, P. Ronney, “Lagrangian, Game Theoretic and PDE Methods for Averaging G-equations in Turbulent Combustion: Existence and Beyond”, Bulletin of Amer. Math Soc., vol. 61, no. 3, pp. 470-514, 2024.
- X. Dai, M. Hu, J. A. Zhou, “An Augmented Subspace Based Adaptive Proper Orthogonal Decomposition Method for Time Dependent Partial Differential Equations”, J. Computational Physics, pp. 113231, 2024.
- E. Dayag, K. Bui, F. Park, J. Xin, “An Image Segmentation Model with Transformed Total Variation”, 2024. Presented at European Signal Processing Conference, Aug, 2024.
- N. T. V. Tran, J. Xin, G. Zhou, “FWin transformer for dengue prediction under climate and ocean influence”, 2024. Presented at International Conference on Machine Learning, Optimization, and Data Science, Sept, 2024.
- Y. Zheng, Z. Xu, F. Xue, B. Yang, J. Lyu, S. Zhang, Y. Qi, J. Xin, “AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs”, 2024. Presented at International Symposium of Visual Computing, 2024.
- K. Bui, Y. Lou, F. Park, J. Xin, “Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising”, pp. 1020-1024, 2023. Presented at IEEE International Conference on Image Processing (ICIP), 2023.
- Z. Long, P. Yin, J. Xin, “Recurrence of Optimum for Training Weight and Activation Quantized Networks”, Applied and Computational Harmonic Analysis, vol. 62, pp. 41-65, 2023.
- K. Bui, Y. Lou, F. Park, J. Xin, “Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise”, Frontiers in Computer Science, vol. 5, pp. 1131317, 2023.
- Z. Li, B. Yang, P. Yin, Y. Qi, J. Xin, “Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data”, IEEE Access, vol. 11, pp. 78042-78051, 2023.
- Y. Liu, J. Xin, “Synchronized Front Propagation and Delayed Flame Quenching in Strain G-equation and Time-Periodic Cellular Flows”, Minimax Theory and its Applications, vol. 8, no. 1, pp. 109-119, 2023.
- W. Whiting, B. Wang, J. Xin, “Convergence of Hyperbolic Neural Networks under Riemannian Stochastic Gradient Descent”, Comm. on Applied Math and Computation, 2023.
- K. Bui, F. Xue, F. Park, Y. Qi, J. Xin, “A Proximal Algorithm for Network Slimming”, 2023. Presented at 9th International Conference on Machine Learning, Optimization and Data Science, Sept, 2023.
- C. Kao, Y. Liu, J. Xin, “A Semi-Lagrangian Computation of Front Speeds of G-equation in ABC and Kolmogorov Flows with Estimation via Ballistic Orbits”, SIAM Interdisciplinary J. Multiscale Modeling & Simulation, vol. 20, no. 1, pp. 107-117, 2022.
- Y. Sun, J. Xin, “Lorentzian Peak Sharpening and Sparse Blind Source Separation for NMR Spectroscopy”, Signal, Image & Video Processing, vol. 16, no. 3, pp. 633-641, 2022.
- Y. Sun, K. Huang, J. Xin, “Structure Assisted NMF Methods for Separation of Degenerate Mixture Data with Application to NMR Spectroscopy”, International Journal of Mathematics and Computation, vol. 33, no. 1, 2022.
Most Cited Publications
- J. Xin, “Front Propagation in Heterogeneous Media”, SIAM Review, vol. 42, no. 2, pp. 161-230, 2000.
- P. Yin, Y. Lou, Q. He, J. Xin, “Minimization of l1−2 for compressed sensing”, SIAM J. Sci. Computing, vol. 37, no. 1, pp. A536-A563, 2015.
- P. Yin, J. S. Zhang, S. Osher, Y-Y. Qi, J. Xin, “Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets”, International Conference on Learning Representations (ICLR), 2019.
- Y. Lou, T. Zeng, S. Osher, J. Xin, “A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing”, SIAM J. Imaging Sci, vol. 8, no. 3, pp. 1798-1823, 2015.
- Y. Lou, P. Yin, Q. He, J. Xin, “Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of L1 and L2”, J. Scientific Computing, vol. 64, no. 1, pp. 178-196, 2015.
- E. Esser, M. Möller, S. Osher, G. Sapiro, J. Xin, “A Convex Model for Nonnegative Matrix Factorization and Dimensionality Reduction on Physical Space”, IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3239–3252, 2012.
- E. Esser, Y. Lou, J. Xin, “A Method for Finding Structured Sparse Solutions to Non-negative Least Squares Problems with Applications”, SIAM Journal on Imaging Sciences, vol. 6, no. 4, pp. 2010–2046, 2013.
- Xin, J., “Existence of Planar Flame Fronts in Convective-Diffusive Periodic Media”, Arch. Rat. Mech. and Anal., vol. 121, pp. 205-233, 1992.
- Xin, J., “Existence and nonexistence of traveling waves and reaction-diffusion front propagation in periodic media”, J. of Stat. Phys., vol. 73, pp. 893-926, 1993.
- S. Zhang, J. Xin, “Minimization of transformed L1 penalty: Theory, Difference of Convex Function Algorithm, and Robust Application in Compressed Sensing”, Mathematical Programming Series B, vol. 169, no. 1, pp. 307-336, 2018.
Contact Information
Website: http://math.uci.edu/~jxin
Email: jxin@math.uci.edu
Phone: (949) 824
Address: Department of Mathematics UC Irvine Irvine, CA 92697, USA
Return to topThis profile was last updated on 2/7/2025.