Abstract: This lecture explores the topics and areas that have guided my research in computational mathematics and deep learning in recent years. Numerical methods in computational science are essential for comprehending real-world phenomena, and deep neural networks have achieved state-of-the-art results in a range of fields. The rapid expansion and outstanding success of deep learning and scientific computing have led to their applications across multiple disciplines. In this lecture, I will focus on connecting machine learning with applied mathematics, specifically discussing topics such as generative models in computational materials and scientific machine learning for solving PDEs.

Bio: Youngjoon Hong received his Ph.D. in mathematics and minor in scientific computing from Indiana University in 2015. From 2015 to 2018, he worked as a Research Assistant Professor at the University of Illinois Chicago. From 2018 to 2021, he served as an Assistant Professor in the Department of Mathematics and Statistics at San Diego State University before joining Sungkyunkwan University. Starting August 2023, he will be an associate professor in the Department of Mathematical Sciences at KAIST.