Teaching
My teaching covers macroeconomics and computational economics, machine learning, deep learning, and reinforcement learning for economists, and interdisciplinary courses that connect AI methods with economic analysis.
Teaching and Course Development
Digital Development of Principles of Macroeconomics
Contribute to the digital textbook, including sample-chapter writing, interactive experiments, and the development of supporting teaching resources.
PKU–Zurich Summer School on Machine Learning and Macro-Finance
Prepared bilingual hands-on guides covering TensorFlow, PyTorch, and JAX environment setup and Jupyter Notebook use, and provided in-class guidance and debugging support for Professor Serguei Maliar's course.
Principles of Macroeconomics (Undergraduate)
Led recitation teaching and course coordination; helped design assignments, computational exercises, and exam materials; and developed interactive Solow-model teaching notes and a Jupyter Notebook laboratory linking derivations, parameter experiments, visualization, and policy discussion.
Computational Economics (Graduate)
Assisted the instructor in preparing course materials, provided hands-on lab support and code guidance, and contributed to assignment design.
Artificial Intelligence and Society (Undergraduate)
Supported instruction on AI foundations and social impacts, including interdisciplinary classroom discussion.
Courses Prepared to Teach
Economics core
- Principles of Macroeconomics; Intermediate and Advanced Macroeconomics
- Mathematical Economics; Dynamic Optimization
- Computational Economics
Artificial intelligence for economists
- Machine Learning; Deep Learning; Reinforcement Learning
Economics × AI
- AI Methods for Solving Dynamic Economic Models; Multi-Agent Learning and Economic Simulation