Research Highlights
Selected papers and research directions presented in a compact format
Research Themes
core agendaMethods & Data
The 2021 container shipping crisis and its consequences for U.S. agricultural exports
Research question: How did the container shipping crisis reshape U.S. agricultural export performance?
Approach: Combined trade-flow evidence with shipping-disruption context to trace how logistics shocks translated into export losses.
Contribution: Frames a logistics crisis as a measurable trade shock with direct implications for agricultural exporters and policy.
Global container shipping disruptions, pop-up ports, and U.S. agricultural exports
Research question: Can port adaptation strategies offset the damage from global shipping disruptions?
Approach: Connected disruption patterns, port responses, and export outcomes to study how trade systems adapt under stress.
Contribution: Presents your work as systems-oriented research with clear relevance to logistics resilience and agricultural trade.
Work environment and intimate partner violence against women: Evidence from China
Research question: How do workplace conditions affect exposure to intimate partner violence?
Approach: Uses applied microeconomic reasoning to connect labor conditions and household outcomes in a socially consequential setting.
Contribution: Balances the trade papers with a strong social-outcomes example and keeps the emphasis on your main research agenda.
U.S. public perceptions of food date labeling: Text mining and content analysis of USDA RFI responses
Research question: What do large-scale public comments reveal about how consumers interpret food date labels?
Approach: Used text mining and structured content analysis to convert unstructured USDA responses into interpretable policy themes.
Contribution: Shows how large-scale text data can support policy-oriented research without displacing the central substantive question.
Machine learning-assisted abstract screening on learning analytics: A step-by-step tutorial
Research question: How can abstract screening in systematic reviews be made faster and more reproducible?
Approach: Turned screening into a workflow paper by documenting ML-assisted prioritization, review design, and practical decision rules.
Contribution: Keeps big-data and ML work visible as part of your research background, without making it the dominant identity.