Zhang Ye Job Title: Professor E-mail: ye.zhang@bit.edu.cn
Professor and doctoral supervisor of Beijing Institute of Technology and Shenzhen Beili Moscow University, Executive Director of the Great Beili - Deep Beili Joint Research Center for Computational Mathematics and Control. Winner of the Kovalevskaya Prize of the World Congress of Mathematicians (2022), winner of the National High-level Young Talent Program (2020), and Humboldt Scholar of Germany (2017). In 2014, he received his PhD in Mathematical Physics from Moscow State University. His research interests are mathematical modeling of inverse problems in mathematical physics, mathematical theory and scientific computation. He has published more than 30 high-level papers in the top international journals of applied mathematics and statistics. At present, he has presided over a number of provincial and ministerial projects such as the National Key Research and Development Young Scientist Project, Beijing Key Project, National Natural Science Foundation Project, Guangdong Province and Shenzhen City.
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February 2014: Moscow University, Associate Doctor (Ph.D.), Mathematical Physics (01.01.03)

September 2019 - Present: Associate Professor and Associate Professor, Beijing Institute of Technology
February 2018 - September 2019: Humboldt Scholar/Postdoctoral Fellow, Chemnitz University of Technology, Germany
December 2017 - February 2020: Senior Research Fellow, University of Orebro, Sweden
June 2016 - November 2017: Researcher, Karlstad University, Sweden
May 2014 - May 2016: Postdoctoral Fellow, Orebro University, Sweden

[49] Chen D., Li J., Zhang Y.*, A posterior contraction for Bayesian inverse problems in Banach spaces. Inverse Problems. 2024. To appear, DOI:10.1088/1361-6420/ad2a03.
[48] Wang Y., Huang Q., Yao Z., Zhang Y.*, On a class of linear regression methods, Journal of Complexity, 2024, 82, 101826.
[47] Zhang Y., Chen C., Stochastic linear regularization methods: random discrepancy principle and applications, Inverse Problems, 2024, 40, 025007.
[46] K. Zhu, Z. Shen, M. Wang, L. Jiang, Y. Zhang, T. Yang, H. Zhang, M. Zhang. Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis. Journal of Computer Assisted Tomography. 2024. To appear, DOI:10.1097/RCT.0000000000001585.
[45] Chaikovskii D., Zhang Y.*, Solving forward and inverse problems involving a nonlinear three-dimensional partial differential equation via asymptotic expansions. IMA Journal of Applied Mathematics, 2023, 88, 525-557.
[44] Chaikovskii D., Liubavin A., Zhang Y.*, Asymptotic expansion regularization for inverse source problems in two-dimensional singularly perturbed nonlinear parabolic PDEs. CSIAM Transactions on Applied Mathematics, 2023,4(4), 721-757.
[43] Huang Q., Gong R., Jin Q., Zhang Y., A Tikhonov regularization method for Cauchy problem based on a new relaxation model. Nonlinear Analysis: Real World Applications, 2023, 74, 103935.
[42] Ran Q., Cheng X., Gong R., Zhang Y., A dynamical method for optimal control of the obstacle problem. Journal of Inverse and Ill-Posed Problems, 2023; 31(4): 577–594.
[41] Su J., Yao Z., Li C., Zhang Y., A Statistical Approach of Estimating Adsorption Isotherm Parameters in Gradient Elution Preparative Liquid Chromatography. Annals of Applied Statistics, 2023, 17(4), 3476-3499.
[40] Shcheglov A., Li J., Wang C., Ilin A., Zhang Y., Reconstructing the Absorption Function in a Quasi-Linear Sorption Dynamic Model via an Iterative Regularizing Algorithm, Advances in Applied Mathematics and Mechanics, 2023, 16(1), 1-16.
[39] Gong R., Wang M., Huang Q., Zhang Y., A CCBM-based generalized GKB iterative regularization algorithm for inverse Cauchy problems. Journal of Computational and Applied Mathematics, 2023, 432(1), 115282.
[38] Chen D., Li J., Zhang Y., Convergence rates of stationary and non-stationary asymptotical regularization methods for statistical inverse problems in Banach spaces. Communications on Analysis and Computation, 2023, 1, 32-55.
[37] Zhang Y., On the acceleration of optimal regularization algorithms for linear ill-posed inverse problems. Calcolo, 2023, 60, 1, Article number: 6.
[36] Zhang Y., Chen C.. Stochastic asymptotical regularization for linear inverse problems. Inverse Problems, 2023, 39, 015007.
[35] Lysak T., Zakharova I., Kalinovich A., Zhang Y., Two-color self-similar laser beams in active periodic structures with Pt-symmetry and quadratic nonlinearity, AIP Conf. Proc., 2023, 2872, 060003.
[34] Abramyan M., Melnikov B., Zhang Y., Some more on restoring distance matrices between DNA chains: reliability coefficients. Cybernetics and Physics, 2023, 12(4), 237–251.
[33] Melnikov B., Zhang Y., Chaikovskii D.. An Algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing, Journal of Biosciences and Medicines, 2023, 11, 310-320.
[32] Chaikovskii D., Zhang Y.*. Convergence analysis for forward and inverse problems in singularly perturbed time-dependent reaction-advection-diffusion equations. Journal of Computational Physics, 2022, 470, 111609.
[31] Hu B., Qian K., Zhang Y., Shen J., Schuller B, The Inverse Problems for Computational Psychophysiology: Opinions and Insights. Cyborg and Bionic Systems, 2022, 2022, 9850248.
[30] Melnikov B., Zhang Y., Chaikovskii D., An inverse problem for matrix processing: an improved algorithm for restoring the distance matrix for DNA chains. Cybernetics and Physics, 2022, 11(4), 217–226.
[29] Yang J., Xu C., Zhang Y.. Reconstruction of the S-Wave Velocity via Mixture Density Networks With a New Rayleigh Wave Dispersion Function. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 035004.
[28] Xu C., Zhang Y.*. Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets. Inverse Problems, 2022, 38, 035004.
[27] Xu C., Zhang Y.*. Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network. Journal of Inverse and Ill-Posed Problems, 2022, 30(5), 693-712.
[26] Dong G. Hintermuller M, Zhang Y. A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging. SIAM Journal on Imaging Sciences, 2021, 14, 645-688.
[25] Zhang Y, Hofmann B. Two new non-negativity preserving iterative regularization methods for ill-posed inverse problems. Inverse Problems and Imaging, 2021, 15, 229-256.
[24] Zhang Y, Gong R. Second order asymptotical regularization methods for inverse problems in partial differential equations. Journal of Computational and Applied Mathematics, 2020, 375.
[23] Gong R, Hofmann B, Zhang Y*. A new class of accelerated regularization methods, with application to bioluminescence tomography. Inverse Problems, 2020, 36, 055013.
[22] Baravdish G, Svensson O, Gulliksson M, Zhang Y*. Damped second order flow applied to image denoising. IMA Journal of Applied Mathematics, 2019, 84, 1082–1111.
[21] Zhang Y, Yao Z, Forssen P, Fornstedt T. Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach. Annals of Applied Statistics, 2019, 13, 2011-2042.
[20] Zhang Y, Hofmann B, On fractional asymptotical regularization of linear ill-posed problems in Hilbert spaces. Fractional Calculus and Applied Analysis, 2019, 22, 699-721.
[19] Zhang Y*, Hofmann B, On the second-order asymptotical regularization of linear ill-posed inverse problems. Applicable Analysis, 2020, 99, 1000–1025. (该杂志历史最受欢迎文章之一、排名第一；高被引论文) https://www.tandfonline.com/doi/full/10.1080/00036811.2018.1517412
[18] Zhang Y*, Gong R, Gulliksson M, Cheng X. A coupled complex boundary expanding compacts method for inverse source problems. Journal of Inverse and Ill-Posed Problems, 2018, 27, 67-86.
[17] Zhang Y*, Gong R, Cheng X, Gulliksson M. A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations. Inverse Problems, 2018, 34, 065001.
[16] Lin G, Cheng X, Zhang Y*. A parametric level set based collage method for an inverse problem in elliptic partial differential equations. Journal of Computational and Applied Mathematics, 2018, 340, 101-121.
[15] Zhang Y*, Forssen P, Fornstedt T, Gulliksson M, Dai X. An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data. Inverse Problems in Science and Engineering, 2018, 26, 1464-1489.
[14] Dai X, Zhang C, Zhang Y, Gulliksson M. Topology optimization of steady Navier-Stokes flow via a piecewise constant level set method. Structural and Multidisciplinary Optimization. 2018, 57, 2193-2203.
[13] Yao Z, Zhang Y, Bai Z, Eddy W. Estimating the number of sources in magnetoencephalography using spiked population eigenvalues. Journal of the American Statistical Association, 2018, 113, 505-518.
[12] Cheng X, Lin G, Zhang Y, Gong R, Gulliksson M. A modified coupled complex boundary method for an inverse chromatography problem. Journal of Inverse and Ill-Posed Problems, 2018, 26, 33-49.
[11] Lin G, Zhang Y*, Cheng X, Gulliksson M, Forssen P, Fornstedt T. A regularizing Kohn-Vogelius formulation for the model-free adsorption isotherm estimation problem in chromatography. Applicable Analysis, 2018, 97, 13-40.
[10] Zhang Y*, Lin G, Gulliksson M, Forssen P, Fornstedt T, Cheng X. An adjoint method in inverse problems of chromatography. Inverse Problems in Science and Engineering, 2017, 25(8), 1112-1137.
[9] Zhang Y*, Lin G, Forssen P, Gulliksson M, Fornstedt T, Cheng X. A regularization method for the reconstruction of adsorption isotherms in liquid chromatography. Inverse Problems, 2016, 32(10), 105005.
[8] Gulliksson M, Holmbom A, Persson J, Zhang Y*. A separating oscillation method of recovering the G-limit in standard and non-standard homogenization problems. Inverse Problems, 2016, 32(2), 025005.
[7] Zhang Y*, Gulliksson M, Hernandez Bennetts V, Schaffernicht E. Reconstructing gas distribution maps via an adaptive sparse regularization algorithm. Inverse Problems in Science and Engineering, 2016, 24(7), 1186-1204.
[6] Zhang Y*, Lukyanenko D, Yagola A. Using Lagrange principle for solving two-dimensional integral equation with a positive kernel. Inverse Problems in Science and Engineering. 2016, 24(5), 811-831.
[5] Zhang Y*, Lukyanenko D, Yagola A. An optimal regularization method for convolution equations on the sourcewise represented set. Journal of Inverse and Ill-Posed Problems. 2016, 23(5), 465-475.
[4] Chen T, Gatchell M, Stockett M, Alexander J, Zhang Y, et al. Absolute fragmentation cross sections in atom-molecule collisions: scaling laws for non-statistical fragmentation of polycyclic aromatic hydrocarbon molecules. The Journal of Chemical Physics. 2014, 140(22) , 224-306.
[3] Zhang Y, Lukyanenko D, Yagola A. Using Lagrange principle for solving linear ill-posed problems with a priori information. Numerical Methods and Programming. 2013, 14, 468-482. (in Russian)
[2] Wang Y, Zhang Y*, Lukyanenko D, Yagola A. Recovering aerosol particle size distribution function on the set of bounded piecewise-convex functions. Inverse Problems in Science and Engineering, 2013, 21, 339-354.
[1] Wang Y, Zhang Y*, Lukyanenko D, Yagola A. A method of restoring the restoring aerosol particle size distribution function on the set of piecewise-convex functions. Numerical Methods and Programming, 2012, 13, 49-66. (in Russian)