PKU-Zurich PhD Summer School on
Machine Learning for Macroeconomics and Finance 北京大学–苏黎世大学
机器学习与宏观金融博士生暑期讲习班

July 6–10, 2026 · Beijing, China 2026 年 7 月 6–10 日 · 中国，北京

About the Program 讲习班简介

The PKU–Zurich PhD Summer School on Machine Learning for Macroeconomics and Finance is an international academic program focusing on frontier applications of machine learning in macroeconomics and macro-finance. The program convenes world-leading faculty to provide rigorous, systematic training in deep learning, reinforcement learning, and discrete- and continuous-time methods, with applications to heterogeneous-agent models, asset pricing, macro-finance, and dynamic general equilibrium.

北京大学–苏黎世大学 PhD Summer School on Machine Learning for Macroeconomics and Finance 是由北京大学与苏黎世大学学者联合主办的高端国际博士生暑期讲习班，聚焦机器学习方法在宏观经济学与宏观金融中的前沿应用。讲习班邀请来自世界一流大学的顶尖学者，系统讲授深度学习、强化学习与离散/连续时间方法在异质性主体模型、资产定价、宏观金融与动态一般均衡中的最新理论进展与计算工具，旨在帮助青年学者掌握前沿研究范式，推动跨学科基础研究。

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Class Format 授课形式
In-person (Beijing) 线下授课（北京）
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Language 授课语言
English 全英文授课
Post-Summer School Event 讲习班后续重点会议

Conference on Machine Learning for Macroeconomics and Finance 机器学习与宏观金融学术会议

July 11–12, 2026 | Peking University 2026年7月11–12日 | 北京大学

📢 Call for papers is coming soon! 📢 征稿启事 (Call for Papers) 即将发布！

Conference Details 查看会议详情

Organizing Committee 组织委员会

  • ▪ Felix Kubler (University of Zurich)Felix Kubler (University of Zurich，北京大学经济学院荣誉教授)
  • ▪ Bo Li (Peking University)李博 (北京大学)
  • ▪ Yucheng Yang (University of Zurich)杨雨成 (University of Zurich)
Peking University University of Zurich

Instructors and Speakers 授课教师与演讲嘉宾

Weinan E

Prof. Weinan E

Peking University; CAS Fellow北京大学，中科院院士

Keynote: AI for Science主旨演讲: AI for Science

 Goutham Gopalakrishna

Prof. Goutham Gopalakrishna

University of Toronto, RotmanUniversity of Toronto, Rotman

 Felix Kubler

Prof. Felix Kubler

University of ZurichUniversity of Zurich

 Serguei Maliar

Prof. Serguei Maliar

Santa Clara UniversitySanta Clara University

 Benjamin Moll

Prof. Benjamin Moll

LSELSE

Special Online Lecture特别线上讲座

 Jonathan Payne

Prof. Jonathan Payne

Princeton UniversityPrinceton University

 Simon Scheidegger

Prof. Simon Scheidegger

HEC LausanneHEC Lausanne

 Yucheng Yang

Prof. Yucheng Yang

University of ZurichUniversity of Zurich

Topics and Course Coverage 教学内容与方向

❖ Foundations of deep learning and computational tools
❖ Heterogeneous-agent macroeconomic models
❖ Maliar–Maliar–Winant methods
❖ Deep Equilibrium Networks
❖ DeepHAM and structural reinforcement learning
❖ Continuous-time macro-finance and PDE methods
❖ Frontier applications in macroeconomics and finance
❖ 深度学习基础与计算工具
❖ 异质性主体宏观模型（Heterogeneous Agent Models）
❖ Maliar–Maliar–Winant 方法
❖ Deep Equilibrium Nets
❖ DeepHAM 与结构化强化学习
❖ 连续时间宏观金融与 PDE 方法
❖ 前沿应用专题
📌 All lectures are complemented by hands-on tutorials, emphasizing a complete workflow from concepts to code, and from economic models to numerical implementation. 📌 课程配套 hands-on tutorials，强调从理论到代码、从模型到计算的完整学术训练。