Yinchu Zhu

Yinchu Zhu is an Assistant Professor of Economics at Brandeis University. His research focuses on the intersection of econometrics, statistics, and machine learning, with a particular emphasis on high-dimensional models, causal inference, and big data analysis. Applications of his work extend to economics, finance, and policy evaluation.

His research interests include statistical inference in complex models, conformal prediction, and the development of robust methods for counterfactual analysis and synthetic controls. He has contributed to methodologies for comparing forecasting performance across models and addressing challenges in high-dimensional data analysis.

Key contributions include exact conformal inference techniques for counterfactual scenarios, testing frameworks for high-dimensional linear models, and methodologies to ensure equity in data-driven hiring practices. His work bridges theoretical advancements with practical applications in policy and economics.

His publications appear in leading journals such as the Annals of Statistics, Journal of the American Statistical Association, and Proceedings of the National Academy of Sciences.