Research Interest: 

Scientific machine learning,  scientific computing, math biology 

Employment:

Postdoctoral researcher, Department of Mathematics, Dartmouth College, Hanover, NH, USA (2020.9-present) 

Postdoctoral researcher, Department of Mathematics, University of Toronto, Toronto, ON, Canada (2019.8-2020.8) 

Industrial Employment:

Senior data scientist, Samsung Fire & Marine Insurance,  Seoul, South Korea (2017.7-2019.8)

Education:

PhD in Mathematics, Courant Institute, New York University, New York, NY, USA (2011.9-2017.8)  - advisor: Charles Peskin

MS in Mathematics, KAIST, Daejeon, South Korea (2010.9-2011.8)

BS in Mathematics, KAIST, Daejeon, South Korea (2004.3-2010.8, military leave 2007.7-2009.6) - summa cum laude 

Publications [Google scholar]: 

Robust Fourier neural networks

Submitted, available at: arXiv:2409.02052 

A stochastic approach for elliptic problems in perforated domains

Journal of Computational Physics, 519, 113426, 2024 [more]

Learning in-between imagery dynamics via physical latent spaces

SIAM Journal on Scientific Computing, 46 (5), C608-C632, 2024 [movies]

An analysis of the derivative-free loss method for solving PDEs

Submitted, available at: arXiv:2309.16829

Weighted inhomogeneous regularization for inverse problems with indirect and incomplete measurement data

Submitted, available at: arXiv:2307.10448 

A neural network approach for homogenization of multiscale problems

SIAM Multiscale Modeling and Simulation, 21:2, 716-734, 2023

Hierarchical learning to solve partial differential equations using physics-informed neural networks

 Lecture Notes in Computer Science, 10475, 548-562, Springer Nature Switzerland ,  The International Conference on Computational Science (ICCS 2023)

Inhomogeneous regularization with limited and indirect data

Journal of Computational and Applied Mathematics, 428, 115193, 2023 , appeared in special issue 'Computational Methods and Models in Deep Learning for Inverse Problems' 

A derivative-free method for solving elliptic partial differential equations with deep neural networks

 Journal of Computational Physics, 419 (15), 109672, 2020

Spontaneous oscillation and fluid-structure interaction of cilia

 Proceedings of the National Academy of Sciences (PNAS), 115 (17) 4417-4422, 2018 [movies]

The intrinsic bounds on the risk premium of Markovian pricing kernels

Finance Research Letters 13, 36-44,2015


Programming:

Proficient in Python (TensorFlow 1 & 2, OpenCV), experienced in CUDA (cuDNN), C++ (OpenCV), C# (EMGU), old experienced in Fortran (BLAS, LAPACK, FFTW)