Wenge Guo

Wenge Guo is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on statistical methodology, particularly in multiple testing procedures, conformal prediction, false discovery rate control, and ordinal classification. He has contributed to advancements in methodologies for clinical trials, machine learning applications, and algorithmic statistical inference.

His research interests span areas such as familywise error rate control, distribution-free prediction methods, and adaptive testing strategies. Recent work emphasizes conformal prediction frameworks for ordinal and multi-label classification, as well as two-stage risk control in ranked retrieval systems. Guo’s expertise also extends to applications in biomedical research and information retrieval systems.

• Key Research Areas: Multiple Testing, Conformal Prediction, Ordinal Classification, Clinical Trials, Statistical Methodology
• Key Contributions: Development of the MHTdiscrete package, hierarchical testing procedures for non-inferiority trials, and adaptive FWER/FDR controls under block dependence.

Notable projects include a National Science Foundation-funded study on large-scale multiple testing problems (2013–2016). His work bridges theoretical statistics with practical applications in machine learning and biomedical research.