I am an economist working at the intersection of Computational Economics, Industrial Organization, and Financial Markets. My research develops computational and machine learning approaches to study how firm strategies and consumer interactions shape outcomes in data-driven and networked industries, with a focus on ICT markets. I combine theoretical modeling with large-scale empirical analysis to make complex market mechanisms tangible, and to bring that same clarity into the classroom. 

My academic journey has been deliberately international and interdisciplinary. I earned my Ph.D. in Economics from Bielefeld University (jointly with Università Cattolica del Sacro Cuore, Milan) as part of the EPOC Marie Skłodowska-Curie doctoral network, after completing an M.A. in Business and Economics at the University of Zurich (major in Banking & Finance, minor in Data Science, magna cum laude) and a double bachelor's degree in Finance and German Language and Literature at Nankai University. This training across economics, data science, and languages has shaped my belief that rigorous quantitative methods and clear communication are equally essential to good research.

My work has appeared in the International Review of Financial Analysis and is forthcoming on SSRN and arXiv, covering topics from fraud detection in financial statements to agentic AI workflows for economic research. My Job Market Paper investigates how firm strategies and consumer interactions shape outcomes in data-driven and networked industries. I am a native Mandarin speaker with C1-level English and German, and in 2025 I delivered a public lecture on "AI Agents in Science" in German at the Wissenswerkstadt Bielefeld science museum, part of my commitment to making research accessible beyond academia. In the 26/27 Job Market, I am seeking positions where I can continue this research agenda while teaching the next generation of economists to work with data and code.