My Profile

Publications and Work in Progress

"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

Experiences and Education 

2017. 09 - 2021. 06 

Dongbei University of Finance and Economics  (China)

Bachelor's Degree in Economics, Major: Finance

Average Scores: 88.7% (Overall Grade 1.8)

2021. 10 - 2023. 09

Rheinische Friedrich-Wilhelms-Universität Bonn

Master of Science Degree in Economics

Average Scores: 1.1/1.0

2023. 10 -

Maastricht University 

PhD student in Economics (external)

2023. 10 -

Max Planck Institute for the Study of Crime, Security and Law 

Ph.D. student of Economics, enrolled in Maastricht University 

Working as Doctoral Researcher in the project MANUNKIND: Determinants and Dynamics of Collaborative Exploitation 

(https://cordis.europa.eu/project/id/101040002)

2020. 07 - 2020. 08

CITIC Securities, Shenyang Branch

Internship of Customer Manager

Carried out an in-depth study of the automatic fund investment, and built Excel modeling through self-learning on the principle of fixed investment

2022. 12 – 2023. 08

Zentrum für Entwicklungsforschung, Bonn

Research Assistant

Data management and visualization for world development and food trade data set

Preparing, cleaning and manipulating data needed for research

Finish a literature review about the implementation of machine learning in the field of food security

Projects 

Final Project of Computational Statistics

(https://github.com/yudingshechu/Computational_Statistics/tree/Final-Project)

Uni Bonn

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 

(https://github.com/yudingshechu/Research-Module-Yield-Curve-Estimation-RKHS)

Uni Bonn

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 

ZEF

Systematically collect, review, and summarize relevant papers, and explain machine learning algorithms in detail

Final Project of Effective Programming Practices for Economists 

(https://github.com/yudingshechu/EPP-Final-Project)

Uni Bonn

Clean and manipulate large volumes of China's national survey data

Replicated a DID paper with an automatically generated project template (pytask)

Master Thesis: Robust Machine Learning for Food Security Forecasting (1.0/1.0) 

(https://github.com/yudingshechu/Masterarbeit)

Uni Bonn

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.