Student Spotlight: Shiyue Zhou MIDS’26

Duke MIDS student Shiyue Zhou is examining how capital flows connect to climate goals in Southeast Asia. 

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As a graduate research assistant for the Climate Dialogue & Innovation Initiative: Southeast Asia and the World, Shiyue Zhou analyzes climate-related financial flows to assess how capital is distributed across mitigation and adaptation projects in Southeast Asia, including distinctions between public, private, and blended finance, with a focus on improving transparency and supporting data-driven analysis and strategic insights.

To learn more about her assistantship experience, we sat down with Shiyue for a brief Q&A.

Q&A

What are your primary responsibilities?

We focus on understanding how climate-related financial flows are tracked and classified in Southeast Asia. A key part of my work involves examining transaction details to assess whether projects meaningfully support climate mitigation or adaptation goals. I evaluate and compare financial databases to understand how climate capital is recorded and reported, and help bring these sources together into a more unified analytical framework. I also translate definitions of public, private, and blended finance into measurable standards and use AI-assisted tools, including Large Language Models, to improve scalability and consistency in investment classification.

What types of skills and experiences have you gained from this research assistantship?

My experience as a research assistant strengthened my technical and analytical skills. Working with financial data from multiple sources made me more attuned to inconsistencies in reporting standards, and deepened my understanding of how climate-related definitions and measurement shape financial analysis and decision-making, with implications for capital allocation. I developed stronger skills in evaluating data quality, reconciling discrepancies, and translating complex financial concepts into clearer analytical frameworks. I also worked on integrating Large Language Models into a structured classification framework to support scalable investment categorization. This work strengthened my ability to build more reliable analytical systems and increased my confidence in explaining technical decisions to collaborators from different backgrounds.

What attracted you to this assistantship?

What attracted me to this assistantship was its focus on climate finance in Southeast Asia, particularly its effort to measure public, private, and blended financial flows in a structured and transparent way. I was especially drawn to how the project examines the relationship between these flows and climate mitigation and adaptation goals. I found that focus both challenging and meaningful. Growing up in Asia, I became interested in how economic growth and climate challenges affect each other and what that means for financial systems. I wanted to apply my quantitative background to something with practical relevance, and this project offered a concrete way to examine how capital flows connect to climate goals through data analysis.

How is this assistantship helping to prepare you to achieve your professional goals?

By analyzing climate finance data and capital structures, I gained a clearer understanding of how financial capital operates in complex investment contexts. Working through complex datasets and classification challenges showed me how quantitative analysis can inform capital allocation and strategic decisions. This experience has prepared me to pursue applied data science or quantitative roles, particularly in areas where analytical systems inform financial or strategic decision-making.

What has been a favorite aspect of your assistantship?

I really enjoy the process of taking complex climate finance concepts and turning them into structured analyses grounded in real data. I appreciate the challenge of moving from ambiguity to clarity and seeing abstract ideas take shape through careful definition and measurement. I also value the opportunity to work with people from public policy, finance, and data science. Conversations across different backgrounds often help me rethink assumptions and refine my approach. Being part of a team that combines rigor with openness to new ideas has been especially rewarding for me.

Shiyue (Cynthia) Zhou is a Master of Interdisciplinary Data Science (Quantitative Finance concentration) candidate at Duke University. With a background in mathematics and economics, she previously worked in data governance and business analytics within the financial services industry, where she developed data validation processes and risk reporting tools that connected technical systems with business operations.

At Duke, she applies quantitative and machine learning methods to complex financial datasets, including climate finance and capital flow analysis in Southeast Asia. Her work brings together financial strategy and climate finance research with a strong quantitative foundation. Zhou continues to deepen her technical expertise, exploring thoughtful applications of data-driven methods to real-world business and policy challenges. She values cross-functional collaboration and works at the intersection of data, strategy, and execution, where analysis informs practical decision-making.