Jongho Park (박종호, 朴鐘浩)

I am a research scientist in CEMSE at KAUST (Mentor: Jinchao Xu). I received a B.S. degree in Mathematical Sciences/Computer Science in 2013, and M.S. and Ph.D. degrees in Mathematical Sciences in 2015 and 2019, respectively (Advisor: Chang-Ock Lee), all from KAIST. From 2019 to 2020, I was a postdoctoral researcher in Department of Mathematical Sciences at KAIST (Mentor: Chang-Ock Lee). From 2020 to 2023, I was a research assistant professor in Natural Science Research Institute at KAIST.

Curriculum Vitae (Last updated: September 8th, 2025), Google Scholar

Research Interests

• Multilevel methods for mathematical optimization: Domain decomposition methods, Subspace correction methods for convex optimization

• Scientific machine learning: Neural network-based PDE solvers, Parallel structures for neural networks

• Numerical methods for computational mechanics: Finite element methods, Nonlinear solvers

• Data-driven methods for dynamical systems: System identification, Dynamic mode decomposition

Publications

Submitted papers

1. Jongho Park and Jinchao Xu. Auxiliary space theory for the analysis of iterative methods for semidefinite linear systems (2025). arXiv

2. Jongho Park and S. Majid Hassanizadeh. Parallel multilevel methods for solving the Darcy-Forchheimer model based on a nearly semicoercive formulation (2025). arXiv

3. Boou Jiang, Jongho Park, and Jinchao Xu. Randomized subspace correction methods for convex optimization (2025). arXiv

4. Boou Jiang, Jongho Park, and Jinchao Xu. Connections between convex optimization algorithms and subspace correction methods (2025). arXiv

5. Young-Ju Lee and Jongho Park. Parallel subspace correction methods for semicoercive and nearly semicoercive convex optimization with applications to nonlinear PDEs (2024). arXiv

Published/Accepted papers

1. Youngkyu Lee, Jongho Park, and Chang-Ock Lee. Balanced group convolution: An improved group convolution based on approximability estimates. Pattern Analysis and Applications. 28, Paper No. 161 (2025). link arXiv

2. Jongho Park and Jinchao Xu. Theory of parallel subspace correction methods for smooth convex optimization. To appear in Domain Decomposition Methods in Science and Engineering XXVIII.

3. Jongho Park. Two-level overlapping Schwarz preconditioners with universal coarse spaces for $2m$th-order elliptic problems. SIAM Journal on Scientific Computing. 46(6), A3681-A3702 (2024). link arXiv

4. Jongho Park. Additive Schwarz methods for fourth-order variational inequalities. Journal of Scientific Computing, 101(3), Paper No. 74 (2024). link arXiv

5. Young-Ju Lee and Jongho Park. On the linear convergence of additive Schwarz methods for the $p$-Laplacian. IMA Journal of Numerical Analysis (2024). link arXiv

6. Jongho Park, Jinchao Xu, and Xiaofeng Xu. A neuron-wise subspace correction method for the finite neuron method. Journal of Computational Mathematics (2024). link arXiv

7. Jinsu Park, Yoonjin Lee, Daewon Yang, Jongho Park, and Hohyun Jung. Artwork pricing model integrating the popularity and ability of artists. AStA Advances in Statistical Analysis. 108(4), 889-913 (2024). link

8. Youngkyu Lee,  Jongho Park, and Chang-Ock Lee. Parareal neural networks emulating a parallel-in-time algorithm. IEEE Transactions on Neural Networks and Learning Systems. 35(5), 6353-6364 (2024). link arXiv

9. Jongho Park. Additive Schwarz methods for semilinear elliptic problems with convex energy functionals: Convergence rate independent of nonlinearity. SIAM Journal on Scientific Computing. 46(3), A1373-A1396 (2024). link arXiv

10. Minwoo Lee, Kyu Tae Kim, and Jongho Park. A numerically efficient output-only system identification framework for stochastically forced self-sustained oscillators. Probabilistic Engineering Mechanics. 74, Paper No. 103516 (2023). link arXiv

11. Minwoo Lee and Jongho Park. An optimized dynamic mode decomposition model robust to multiplicative noise. SIAM Journal on Applied Dynamical Systems. 22(1), 235–268 (2023). link arXiv

12. Chaemin Lee and Jongho Park. Preconditioning for finite element methods with strain smoothing. Computers & Mathematics with Applications. 130, 41–57 (2023). link arXiv

13. Youngkyu Lee,  Jongho Park, and Chang-Ock Lee. Two-level group convolution. Neural Networks. 154, 323–332 (2022). link arXiv

14. Jongho Park. Additive Schwarz methods for convex optimization---convergence theory and acceleration. Domain Decomposition Methods in Science and Engineering XXVI, 715–723, Lecture Notes in Computational Science and Engineering, 145, Springer, 2022. link

15. Chang-Ock Lee, Youngkyu Lee, and Jongho Park. A parareal architecture for very deep neural network. Domain Decomposition Methods in Science and Engineering XXVI, 407–415, Lecture Notes in Computational Science and Engineering, 145, Springer, 2022. link

16. Jongho Park. Fast gradient methods for uniformly convex and weakly smooth problems. Advances in Computational Mathematics, 48, Paper No. 34 (2022). link arXiv

17. Chaemin Lee, Minam Moon, and Jongho Park. A gradient smoothing method and its multiscale variant for flows in heterogeneous porous media. Computer Methods in Applied Mechanics and Engineering, 395, Paper No. 115039 (2022). link

18. Jongho Park. Additive Schwarz methods for convex optimization with backtracking. Computers & Mathematics with Applications, 113, 332–344 (2022). link arXiv

19. Jongho Park. Accelerated additive Schwarz methods for convex optimization with adaptive restart. Journal of Scientific Computing, 89(3), Paper No. 58 (2021). link arXiv

20. Chang-Ock Lee and Jongho Park. A dual-primal finite element tearing and interconnecting method for nonlinear variational inequalities utilizing linear local problems. International Journal for Numerical Methods in Engineering, 122(22), 6455–6475 (2021). link

21. Chaemin Lee and Jongho Park. A variational framework for the strain-smoothed element method. Computers & Mathematics with Applications, 94, 76–93 (2021). link arXiv

22. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. Corrigendum to "A dual iterative substructuring method with a small penalty parameter", [J. Korean Math. Soc. 54 (2017), No. 2, 461–477]. Journal of the Korean Mathematical Society, 58(3), 791–797 (2021). link arXiv 

23. Jongho Park. Pseudo-linear convergence of an additive Schwarz method for dual total variation minimization. Electronic Transactions on Numerical Analysis, 54, 176–197 (2021). link arXiv 

24. Chang-Ock Lee and Jongho Park. Recent advances in domain decomposition methods for total variation minimization. Journal of the Korean Society for Industrial and Applied Mathematics, 24(2), 161–197 (2020). link

25. Jongho Park. An overlapping domain decomposition framework without dual formulation for variational imaging problems. Advances in Computational Mathematics, 46(4), Paper No. 57 (2020). link arXiv

26. Jongho Park. Additive Schwarz methods for convex optimization as gradient methods. SIAM Journal on Numerical Analysis, 58(3), 1495–1530 (2020). link arXiv

27. Chang-Ock Lee and Jongho Park. A finite element nonoverlapping domain decomposition method with Lagrange multipliers for the dual total variation minimizations. Journal of Scientific Computing, 81(3), 2331–2355 (2019). link arXiv

28. Chang-Ock Lee and Jongho Park. Fast nonoverlapping block Jacobi method for the dual Rudin–Osher–Fatemi model. SIAM Journal on Imaging Sciences, 12(4), 2009–2034 (2019). link arXiv

29. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. A finite element approach for the dual Rudin–Osher–Fatemi model and its nonoverlapping domain decomposition methods. SIAM Journal on Scientific Computing, 41(2), B205–B228 (2019). link arXiv

30. Chang-Ock Lee, Changmin Nam, and Jongho Park. Domain decomposition methods using dual conversion for the total variation minimization with $L^1$ fidelity term. Journal of Scientific Computing, 78(2), 951–970 (2019). link

Preprints

1. Jongho Park and Jinchao Xu. DualFL: A duality-based federated learning algorithm with communication acceleration in the general convex regime (2023). arXiv