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沈阳：大帅府
沈阳：北陵
自我介绍
自我介绍
经济学博士研究员，工作单位为德国弗莱堡马克斯普朗克研究所----国际刑法研究所，受雇于欧洲议会项目MANUNKIND，研究课题为现代奴隶制与剥削的原理和涌现秩序，主攻博弈论与应用微观经济学
德国波恩大学经学济硕士，东北财经大学经济学学士（金融方向）
擅长并且对于以下内容感兴趣：计量经济学，数据分析，机器学习，R和Python编程
有R和Python数据分析的工作经验，同时也熟悉STATA和MATLAB
技能：
技能：
计算机与信息技术: R-studio, Python, Microsoft Excel, - World, - Powerpoint, LaTeX, STATA, MATLAB
语言: 汉语普通话（母语），英语（雅思7.0），德语（TELC B1）
软技能：沟通，团队合作，情绪管理，自我激励
出版物与正在进行中的项目
出版物与正在进行中的项目
"How robust are machine learning approaches for improving food security amid crises? - Evidence from COVID-19 in Uganda" (with Lukas Kornher and Clara Brandi), World Development, 196(2025), doi:10.1016/j.worlddev.2025.107171
"Tricked into trouble: Deception, threat, and coercion in exploitative labor relations" (with Maximilian L. Schmitt*, Thomas Meissner, and Hannes Rusch), Working Paper (2025), preprint doi: 10.26481/umagsb.2025007
"Modern Slavery and Mistrust: a conceptual replication of Nunn & Wantchekon (AER, 2011)" (with Hannes Rusch), Working Paper (2025), won the second prize for the best PhD student paper at the IAREP conference, Tartu 2025. preprint doi: 10.26481/umagsb.2025008
教育经历与经验
教育经历与经验
2017. 09 - 2021. 06
东北财经大学
经济学学士，金融方向（国际商学院）
平均分：88.7/100
2021. 10 - 2023. 09
波恩大学（德国）
经济学硕士
平均分：1.1/1.0 （毕业论文：1.0）
注：德国分数系统1.0为满分，4.0为及格线，详细对照可以谷歌搜索：german grading system to gpa
2023. 10 - 现在
马斯特里赫特大学（荷兰）
经济学博士生
2023. 10 - 现在
马克斯普朗克研究所（前国际刑法研究所）（德国弗莱堡）
经济学博士研究员，博士学位挂名在荷兰马斯特里赫特大学（导师所在学校）
受雇于项目：MANUNKIND: Determinants and Dynamics of Collaborative Exploitation
(https://cordis.europa.eu/project/id/101040002)
2020. 07 - 2020. 08
中信证券（沈阳奉天街营业部）
客户经理实习
学习中信证券券商业务
2022. 12 – 2023. 08
发展研究中心（波恩）ZEF
研究助理
国际发展和食品贸易数据管理和数据可视化
完成了一篇关于机器学习技术在粮食安全领域应用的文献综述
项目经历与作品
项目经历与作品
Final Project of Computational Statistics
Final Project of Computational Statistics
(https://github.com/yudingshechu/Computational_Statistics/tree/Final-Project)
波恩大学
Used simulated data to implement LASSO in instrumental regression
Compared the post-LASSO instrumental regression algorithm in different simulated cases
Term Paper of Research Module in Econometrics and Statistics
Term Paper of Research Module in Econometrics and Statistics
(https://github.com/yudingshechu/Research-Module-Yield-Curve-Estimation-RKHS)
波恩大学
Replicated the implementation of RKHS non-parametric estimation in yield curve estimation
Used simulated data to show the advantages of RKHS estimation over other traditional estimations
Literature Review of machine learning implementation in food security
Literature Review of machine learning implementation in food security
发展研究所
Systematically collect, review, and summarize relevant papers, and explain machine learning algorithms in detail
Final Project of Effective Programming Practices for Economists
Final Project of Effective Programming Practices for Economists
(https://github.com/yudingshechu/EPP-Final-Project)
波恩大学
Clean and manipulate large volumes of China's national survey data
Replicated a DID paper with an automatically generated project template (pytask)
硕士论文: Robust Machine Learning for Food Security Forecasting (1.0/1.0)
硕士论文: Robust Machine Learning for Food Security Forecasting (1.0/1.0)
(https://github.com/yudingshechu/Masterarbeit)
波恩大学
The food insecurity issue is serious in eastern Africa, facing the shocks such as the Covid pandemic and war in Ukraine, a robust machine learning is needed for food insecurity forecasting. This study combines the Uganda National Households Survey data and other open-source data to predict household food insecurity before and during Covid. It is shown that models based on decision trees behave robustly when facing the shock of Covid. These tree-based models provide flexibility for policymakers to trade off the cost and benefit of food insecurity aiding. We also find that demographic and asset features provide the most prediction power. Finally, tree-based methods are robust against limited features or un-updated training data when facing a shock, implying they are robust in practical scenarios.
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