I am currently a professor in the Institute of Applied Mathematical Sciences at the National Taiwan University. I mainly working on the inverse problems, partial differential equations, numerical analysis and mathematical modeling. Recently, I am interested in the theoretical foundation of machine learning.
I do not have a lab in the traditional sense. Instead, I supervise several master’s students and meet with them once a week.
Partial differential equations
Inverse problems
Numerical analysis
Bayesian approach
Machine learning
2023, TMS Academic Prize.
2022, Ministry of Education Academic Award.
2018, Distinguished Research Award, Ministry of Science and Technology
2010, Distinguished Teaching Award, National Taiwan University
2010, Distinguished Research Award, National Science Council
2006, Junior Investigator Award, Academia Sinica, Taiwan.
1997 University of Washington, PhD.
Job Description
*Implement numerical methods for PDEs (e.g., finite difference, finite element, or spectral methods)
*Apply machine learning approaches (e.g., neural networks, physics-informed neural networks) to approximate or accelerate PDE solutions
*Analyze accuracy, stability, and computational efficiency of methods
*Assist in data preparation, model training, and result visualization
*Document methodologies, code, and findings clearly
Preferred Intern Educational Level
Senior undergraduate students or master’s students
Skill sets or Qualities
Qualifications: Background in applied mathematics, engineering, computer science, or a related field;
Familiarity with partial differential equations and numerical analysis;
Basic knowledge of machine learning or deep learning concepts;
Programming experience in Python, MATLAB, or C++.