Understanding censoring, which occurs when the event of interest is not observed for some individuals within the study period, is critical for modeling time-to-event data. This is particularly important for applications such as risk prediction in cancer studies, electronic health records, and clinical trials. Ignoring censoring can lead to biased and inaccurate predictive performance. While numerous statistical approaches in survival analysis, such as Cox regression, have been developed to handle censoring, it remains an open challenge to effectively integrate these methods with modern statistical learning techniques for classification. This SUDS project aims to extend the use of Inverse Probability of Censoring Weighting (IPCW) in conjunction with statistical learning to improve risk prediction for right-censored data. Although IPCW has shown promise when integrated with statistical learning methods (e.g., Vock et al., 2016), its predictive performance can suffer when a significant proportion of subjects are censored before the time of interest due to a huge reduction in effective sample sizes. This project will explore new methodological advancements to address these limitations and validate these approaches through simulation studies and real-world applications in cancer genomics. Students working on this SUDS project will meet weekly with the supervisor to discuss progress and address challenges.