Welcome to the Mathematical AI and Scientific Computing (MAISC) Lab at Chung-Ang University!

Join MAISC Lab!

We are looking for highly motivated students in our MAISC lab. If you are interested in joining our MAISC Lab team, please reach out via email to us(jaeyong AT cau DOT ac DOT kr) and schedule an interview. We recommend reaching out sooner rather than later :)

MAISC Lab. News

📢 2025.06: Our joint research proposal, “Analysis-Guided Neural Operator for Inverse Problems” (PI: Prof. Mikyoung Lim), has been selected for the Global Basic Research Laboratory (BRL) (글로벌 기초연구실) program funded by the National Research Foundation (NRF) of Korea.

📢 2025.05: Our paper, Physics-Informed Neural Operators for Tissue Elasticity Reconstruction, is accepted at Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025 [Provisional accept: ~9%].

📢 2025.05: Our paper, Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning, is accepted at Neurocomputing.

📢 2025.03: We are excited to welcome Seomkyun Song, Marc Haltmayer, and Hyeonjun Choi to the MAISC Lab starting in March! We look forward to their contributions and collaboration.

📢 2025.02.: Our paper, Isometric Regularization for Manifolds of Functional Data, is accepted at ICLR 2025 (Project page).

📢 2024.11: Our paper, Finite Element Operator Network for Solving Elliptic-type parametric PDEs, is accepted at SIAM Journal on Scientific Computing (SISC) (Project page).

Mathematical AI and Scientific Computing (MAISC) Lab.

Our MAISC Lab at Chung-Ang University is interested in research related to AI based on mathematical theory (Mathematical AI) and the utilization of AI for scientific problem-solving (Scientific Computing), grounded in mathematical principles. We focus on the following specific topics.

• AI for Science (AI4Science)

• Scientific Machine Learning (SciML)

• Neural Ordinary Differential Equations (Neural ODE)

• Physics-Informed Machine Learning

• Neural PDE solver

  • Physics-Informed Neural Networks (PINNs)

  • Neural Operator

• Data-Driven Physics Modeling

  • Inverse Problem

  • Parameter Estimation

  • Physics Discovery

  • Data-Driven Simulation

This research lab is led by Prof. Jae Yong Lee (Homepage) from the Department of AI at Chung-Ang University. Before joining Chung-Ang University, he served as an AI Research Fellow at the Center for AI and Natural Sciences (CAINS), Korea Institute for Advanced Study (KIAS), South Korea. He received his Ph.D. in the Department of Mathematics at Pohang University of Science and Technology (POSTECH), South Korea.