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: May 4th, 2024), Google Scholar

Research Interests

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

• Scientific machine learning: Federated 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. Two-level overlapping Schwarz preconditioners with universal coarse spaces for $2m$th-order elliptic problems (2024). arXiv

2. Jinsu Park, Yoonjin Lee, Daewon Yang, Jongho Park, and Hohyun Jung. Artwork pricing model integrating the popularity and ability of artists (2023).

3. Youngkyu Lee, Jongho Park, and Chang-Ock Lee. Balanced group convolution: An improved group convolution based on approximability estimates (2023). arXiv

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

5. Jongho Park. Additive Schwarz methods for fourth-order variational inequalities (2023). arXiv

6. Jongho Park, Jinchao Xu, and Xiaofeng Xu. A neuron-wise subspace correction method for the finite neuron method (2022). arXiv

7. Young-Ju Lee and Jongho Park. On the linear convergence of additive Schwarz methods for the $p$-Laplacian (2022). arXiv

Published papers

1. 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

2. 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

3. 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

4. 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

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

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

7. 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

8. 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

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

10. 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

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

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

13. 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

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

15. 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 

16. 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 

17. 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

18. 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

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

20. 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

21. 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

22. 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

23. 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

Slides

1. Neural networks with parallel structures: exploiting mathematical ideas in designing neural networks. Numerical Methods for Data Science and Engineering Seminar, Texas State University, March 25, 2022. slide

2. Additive Schwarz methods for convex optimization—convergence theory and acceleration. CCMA Seminar on Mathematics of Data and Computation, Pennsylvania State University, January 20, 2022. slide

3. Mathematical theory of finite element methods with strain smoothing. Seminar in Department of Applied Mathematics, Kyung Hee University, January 17, 2022. slide

4. An optimized dynamic mode decomposition model robust to multiplicative noise. Seminar in Department of Mathematical Sciences, KAIST, September 13, 2021. slide

5. Fast gradient methods for uniformly convex and weakly smooth problems. Seminar in Department of Mathematical Sciences, KAIST, April 5, 2021. slide

6. Domain decomposition methods for convex optimization in image processing: focusing on total variation minimization. Ph.D. Defense, Department of Mathematical Sciences, KAIST, May 31, 2019. slide