Driven by a passion for mathematical analysis and machine learning, I specialize in developing partial differential equations (PDE)-based algorithms to tackle high-dimensional machine learning challenges. With a Ph.D. in Mathematics from UCLA and a background in applied mathematics, I’ve honed my expertise in optimal transport, gradient flows, and mean field games. Currently, as an IMA-NIST Postdoctoral Fellow at the University of Minnesota, I collaborate on machine learning projects with esteemed professors. My research, teaching, and work experiences underscore my commitment to advancing mathematical theory and its applications in cutting-edge technologies.
Research Interests š§
My research focuses on developing partial differential equations (PDE)-based algorithms to solve high-dimensional machine learning problems and analyze the theoretical properties of the algorithms. My interests include machine learning, generative modeling, optimal transport, gradient flows, and mean field games.
Academic Positions š
University of Minnesota, Twin Cities, Minneapolis, MN IMA-NIST Postdoctoral Fellow | Aug 2022 – Present Joint NIST-IMA Postdoctoral Fellowship in Analysis of Machine Learning at the Institute for Mathematics and its Applications (IMA) in the College of Science and Engineering at the University of Minnesota (UMN). Working on machine learning projects with Prof. Jeff Calder, Prof. Gilad Lerman, and Prof. Li Wang. University of California, Los Angeles, Los Angeles, CA Assistant Adjunct Professor | Jun 2022 – Aug 2022 Taught introductory programming course in C++ (PIC 10A) as the main instructor.
Education š
University of California, Los Angeles, Los Angeles, CA Ph.D. in Mathematics | Sep 2017 – Jun 2022, Advisor: Professor Stanley Osher. Thesis: Algorithms For Optimal Transport And Their Applications To PDEs. George Mason University, Fairfax, Virginia B.S. in Mathematics | May 2015 Concentration in Applied Mathematics and Mathematical Statistics GPA: 3.84/4.0 magna cum laude, Phi Beta Kappa.
Honors and Awards š
2022: Rising Star in Data Science from the University of Chicago. PROFILE LINK, 2021: UCLA Dissertation Year Fellowship ($20,000), 2014: Outstanding Presentation Award at the Joint Mathematical Meetings, Baltimore, MD.
Teaching Experience š
University of Minnesota, Minneapolis, MN Instructor | Aug 2022 ā Present Spring 2024: Math 2243 – Linear Algebra and Differential Equations Spring 2023: Math 2243 – Linear Algebra and Differential Equations University of California, Los Angeles, Los Angeles, CA Teaching Assistant | Aug 2017 ā Jun 2021 PIC 10ABC: Intro, intermediate, advanced C++ programming. PIC 16: Python with Applications – Python modules such as PyQt, SciPy, Pandas, and NLTK. Math 164: Fundamentals of optimization. Linear / nonlinear programming. Math 151B: Applied numerical methods with analysis of algorithms and computer implementations. Mentor from Directed Reading Program (DRP) | Jan 2021 – Mar 2022 Mentoring undergraduate students for the quarter-long independent study project in math. Topics: Unsupervised learning of image segmentation, Generative Adversarial Networks, Applications of mean field games in finance.
Work Experience š¼
University of California, Los Angeles, Los Angeles, CA Research Assistant | Aug 2017 ā Aug 2022Developed a new algorithm to compute the Wasserstein distance between large point clouds. Applications in machine learning models such as generative adversarial networks (GAN). (PyTorch, C++)Developed a fast and accurate algorithm that computes the solution of the Wasserstein gradient flows on 2D or 3D grids. (C++)Developed a new mean-field control model in controlling the propagation of epidemics in response to COVID pandemic. (C++)Studied Regularity theory for minimizers of polyconvex functionals related to incompressible / compressible Navier-Stokes equations under Prof. Wilfrid Gangbo and Prof. Matt Jacobs.George Mason University, Fairfax, VA Research Assistant | May 2017 ā May 2018Developed deep learning methods using SVD and diffusion map for classification tasks. (Tensorflow)Cheiron, Inc., Washington D.C. Actuary | Feb 2015 – Sep 2016Evaluated the likelihood of undesirable events using actuarial pricing and projection models. Worked on actuarial valuation reports for public, single-employer, and multi-employer plans.
Skills š»
Programming: C/C++, Python, Matlab Language: English, Korean