Prager Assistant Professor of Applied Mathematics
Office: Rm 222, 182 George Street
Phone : (401) 863-1320
Email: yeonjong_shin AT brown DOT edu
• Prager Assistant Professor, July 2018 - Present
Brown University, Providence, RI, USA
• Ph.D. Mathematics, May 2018
The Ohio State University, Columbus, OH, USA
Advisor: Dongbin Xiu
• B.S. Mathematics and
• B.A. Economics, August 2013*
Yonsei University, Seoul, South Korea
*Military Service as a KATUSA, 2009-2011
• Mathematics of Machine Learning and Approximation Theory
• Scientific Computing, Stochastic Optimization, Uncertainty Quantification, and Data Science
Publications (google scholar)
 In preparation
 In preparation
 In preparation
 A. D. Jagtap, Y. Shin, K. Kawaguchi, and G. E. Karniadakis, Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
Submitted for publication. https://arxiv.org/pdf/2105.09513
 Y. Shin, J. Darbon, G. E. Karniadakis, A Caputo fractional derivative-based algorithm for optimization
Submitted for publication. https://arxiv.org/abs/2104.02259
 B. Deng, Y. Shin, L. Lu, Z. Zhang, and G. E. Karniadakis, Convergence rate of DeepONets for learning operators arising from advection-diffusion equations,
Submitted for publication. https://arxiv.org/pdf/2102.10621
 Y. Shin, Z. Zhang, G. E. Karniadakis, Error estimates of residual minimization using neural networks for linear PDEs,
Submitted for publication. https://arxiv.org/abs/2010.08019
 Y. Shin, Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Networks,
Submitted for publication. https://arxiv.org/abs/1910.05874
 M. Ainsworth and Y. Shin, Plateau Phenomenon in Gradient Descent Training of ReLU networks: Explanation, Quantification, and Avoidance,
Accepted in SIAM J. Sci. Comput. https://arxiv.org/abs/2007.07213
 J. Hou, Y. Shin, and D. Xiu, Identification of Corrupted Data via k-means Clustering for Function Approximation,
CSIAM Trans. Appl. Math., 2, pp. 81-107 (2021).
 Y. Shin, J. Darbon, G. E. Karniadakis, On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs,
Commun. Comput. Phys., 28, pp. 2042-2074 (2020). https://arxiv.org/abs/2004.01806
 Y. Shin and G. E. Karniadakis, Trainability of ReLU Networks and Data-dependent Initialization,
Journal of Machine Learning for Modeling and Computing, 1(1), 39-74 (2020). https://arxiv.org/abs/1907.09696
 L. Lu, Y. Shin, Y. Su, and G. E. Karniadakis, Dying ReLU and Initialization: Theory and Numerical Examples,
Commun. Comput. Phys., 28, pp. 1671-1706 (2020). https://arxiv.org/abs/1903.06733
 Y. Shin, K. Wu and D. Xiu, Sequential function approximation using randomized samples,
J. Comput. Phys., 371, 363-381 (2018).
 K. Wu, Y. Shin and D. Xiu, A randomized tensor quadrature method for high dimensional polynomial approximation,
SIAM J. Sci. Comput., 39(5), A1811-A1833 (2017).
 Y. Shin and D. Xiu, A randomized algorithm for multivariate function approximation,
SIAM J. Sci. Comput., 39(3), A983-A1002 (2017).
 L. Yan, Y. Shin and D. Xiu, Sparse approximation using l1-l2 minimization and its applications to stochastic collocation,
SIAM J. Sci. Comput., 39(1), A229-A254 (2017).
 Y. Shin and D. Xiu, Correcting data corruption errors for multivariate function approximation,
SIAM J. Sci. Comput., 38(4), A2492-A2511 (2016).
 Y. Shin and D. Xiu, On a near optimal sampling strategy for least squares polynomial regression,
J. Comput. Phys., 326, 931-946 (2016).
 Y. Shin and D. Xiu, Nonadaptive quasi-optimal points selection for least squares linear regression,
SIAM J. Sci. Comput., 38(1), A385-A411 (2016).
 SIAM CSE 2021 (Virtual), March 2021
 SIAM MDS 2020 (Virtual), June 2020
 ICERM: Scientific Machine Learning, Jan 2019, Providence, RI, USA
 SIAM UQ 2018, April 2018, Garden Grove, CA, USA
 SIAM CSE 2017, Feb 2017, Atlanta, GA, USA
 12th WCCM - 6th APCOM 2016, July 2016, Seoul, Korea.
 15th International Conference Approximation Theory, May 2016, San Antonio, TX, USA.
 SIAM UQ 2016, April 2016, Lausanne, Switzerland.
 14th Copper Mountain Conference on Iterative Methods, March 2016, CO, USA.
 ICIAM 2015, August 2015, Beijing, China.
 SIAM CSE 2015, March 2015, Salt Lake City, UT, USA
 (Virtual) University of California, Riverside, CA, USA, May 2021.
 (Virtual) University of Texas at El Paso, Texas, USA, Apr 2021.
 (Virtual) RWTH Aachen University, Germany, Mar 2021.
 (Virtual) University of Iowa, Iowa, USA, Mar 2021.
 (Virtual) KAIST, Daejeon, Korea, Feb 2021.
 (Virtual) Helmholtz-Zentrum Dresden-Rossendorf, Germany, Sep 2020.
 (Virtual) Physics-Informed Learning Machines Webinar, PNNL, May 2020.
 Department of Computational Science & Engineering Seminar, Yonsei University, Aug 2019, Seoul, Korea.
 Department of Mathematical Sciences Seminar, Seoul National University, Aug 2019, Seoul, Korea.
 Department of Mathematics Seminar, Yonsei University, Jan 2019, Seoul, Korea.
 Applied Algebra and Optimization Research Center Seminar, Sungkyunkwan University, Jan 2019, Suwon, Korea.
 Applied Mathematics Colloquium, Brown University, Sep 2018, Providence, RI, USA.
 Spring School, University of South Carolina, Feb 2018, Columbia, SC, USA.
 Department of Mathematics Seminar, Sungkyunkwan University, July 2016, Suwon, Korea.
APMA 1650, APMA 1655 Statistical Inference I, [Lecture Notes]
APMA 1170 Introduction to Computational Linear Algebra
APMA 1210 Operations Research - Deterministic Models
APMA 1360 Applied Dynamical Systems