Wei Biao Wu

Wei Biao Wu is affiliated with the University of Chicago in the United States. Their research spans several disciplines, with a significant focus on fields such as Computer Science, Mathematics, and Economics, Econometrics and Finance. Within these domains, they have contributed notably to subfields including Statistics and Probability, Artificial Intelligence, Finance, Economics and Econometrics, as well as Statistics, Probability and Uncertainty.

Their work covers a diverse set of main topics including Statistical Methods and Inference, Financial Risk and Volatility Modeling, Advanced Statistical Methods and Models, Market Dynamics and Volatility, Advanced Statistical Process Monitoring, Complex Systems and Time Series Analysis, and Stochastic processes and financial applications.

Wei Biao Wu has published extensively in a range of academic venues. Frequent publication outlets include:

• arXiv (Cornell University)
• The Annals of Statistics
• SSRN Electronic Journal
• Journal of the American Statistical Association
• Journal of Econometrics

Their recent papers demonstrate a focus on statistical methodology, stochastic processes, and applications in economics and machine learning. Notable publications include:

• "Mask and Reason", 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• "Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes", 2021, The Annals of Statistics
• "Online Covariance Matrix Estimation in Stochastic Gradient Descent", 2021, Journal of the American Statistical Association
• "Development of a Model Predicting the Outcome of In Vitro Fertilization Cycles by a Robust Decision Tree Method", 2022, Frontiers in Endocrinology
• "Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation", 2021, arXiv (Cornell University)

The scientist collaborates frequently with a core group of co-authors, reflecting interdisciplinary work:

• Yanhong Wu
• Wanrong Zhu
• Ning Wang
• Likai Chen
• Sayar Karmakar

Wei Biao Wu's contributions emphasize rigorous statistical approaches to problems in time series, covariance estimation, and volatility modeling, with applications extending to finance and artificial intelligence. Their publication record reflects engagement with both theoretical and applied research questions across multiple domains within the broader field of statistical science.