Guo Yu

Guo Yu is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His research focuses on high-dimensional statistics, machine learning, and applied probability, with applications to econometrics and healthcare analytics. He holds a PhD from Cornell University.

Education:

• PhD in Statistics, Cornell University

Research Interests:

• Statistical methodologies for high-dimensional data
• Machine learning algorithms with interpretability
• Applications in healthcare prediction (e.g., COVID-19 modeling)
• Econometric models with error control

Recent Work Trends: His articles emphasize robust statistical frameworks for high-dimensional inference, including error variance estimation, interaction modeling, and cost-efficient feature selection. He also explores hybrid models combining autoregressive methods with neural networks for time-series prediction. His work often bridges theoretical statistics and practical applications in cloud computing and large-scale data analysis.

Awards: None listed.

Grants and Advising: No specific grants or advisees listed in the provided texts. His research likely involves collaborations with institutions like Microsoft Azure (as seen in the 2018 Deepview paper).