• July 2022 --- Now, Postdoc Research Associate, Department of Mathematics, Carnegie Mellon University, Mentor: Prof. Hayden Schaeffer (UCLA)
  

• Jan 2021--- June 2022, Visiting Assistant Professor, Department of Mathematics, Purdue University, Mentor: Prof. Guang Lin.
  

• PhD, Mathematics, Texas A&M University, Aug 2016 --- Dec 2020.
  Supervisors: Prof. Yalchin Efendiev and Prof. Eric Chung.
• M.S., Mathematics, University of Alberta, Aug 2014 --- June 2016.
  Thesis Directional Splitting On Grid With Local Refinement For Parabolic Problems.
  Supervisors: Prof. Peter Minev and Prof. Yaushu Wong.
• B.S., Applied Mathematics, Hong Kong Baptist University, Aug 2010 --- Jun 2014.

• Multiscale finite element methods
• Inverse problem
• Deep and reinforcement learning

• Zecheng Zhang Google Scholar.

1. Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer. BelNet: Basis enhanced learning, a mesh-free neural operator. Preprint, 2022.
   A tutorial and programming code of BelNet and operator learning is here. This BelNet tutorial is on Kaggle and we will upload a tutorial on GitHub later.
2. Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou. Fast Tempering for Stochastic Gradient Langevin Dynamics. Preprint, 2022.
3. Na Ou, Zecheng Zhang, Guang Lin, A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems. Preprint, 2022.
4. Yalchin Efendiev, Wing Tat Leung, Wenyuan Li, Zecheng Zhang. Hybrid explicit-implicit learning for multiscale problems with time dependent source. Communications in Nonlinear Science and Numerical Simulation, 2023.
5. Guang Lin, Christian Moya, Zecheng Zhang. On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. Preprint, 2022.
6. Guang Lin, Zecheng Zhang, Zhidong Zhang. Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. Inverse Problems, 2022.
7. Guang Lin, Christian Moya, Zecheng Zhang. Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. Journal of Computational Physics, 2022.
8. Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang. Efficient hybrid explicit-implicit learning for multiscale problems. Journal of Computational Physics, 2022.
9. Wing Tat Leung, Guang Lin, Zecheng Zhang. NH-PINN: Neural homogenization based the physics-informed neural network for the multiscale problems. Journal of Computational Physics, 2022.
10. Guang Lin, Yating Wang, Zecheng Zhang. Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. Journal of Computational Physics, 2022.
11. Liu Liu, Tieyong Zeng, Zecheng Zhang. A deep neural network approach on solving the linear transport model under diffusive scaling. Preprint, 2021.
12. Eric Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang. Computational multiscale methods for parabolic wave approximations in heterogeneous media. Applied Mathematics and Computation, 2022.
13. Eric Chung, Yalchin Efendiev, Wing Tat Leung, Sai-Mang Pun and Zecheng Zhang. Multi-agent reinforcement learning aided sampling algorithms for a class of multiscale inverse problems. Under revision, Journal of Scientific Computing, 2022.
14. Boris Chetverushkin, Eric Chung, Yalchin Efendiev, Sai-Mang Pun and Zecheng Zhang. Computational multiscale methods for quasi-gas dynamic equations. Journal of Computational Physics, 2020.
15. Eric Chung, Wing Tat Leung, Sai-Mang Pun and Zecheng Zhang. A multi-stage deep learning based algorithm for multiscale model reduction. Journal of Computational and Applied Mathematics, 2020.
16. Eric Chung, Yalchin Efendiev, Wing Tat Leung, Zecheng Zhang. Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations. Mathematics, 2020.

• Spring 2023, 21-344 Numerical Linear Algebra.

• Fall 2022, 21-670 Linear Algebra for Data Science.

• Spring 2022, Linear Algebra at Purdue university

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