• Development of a Prognostication Model for Breast Cancer:• Manipulated a large breast screening outcomes dataset (1.6 million records) using SAS and R, employing data cleaning, data linkage, and data analysis to generate descriptive statistic summary reports.• Investigated new methodologies and collaborate with clinical domain experts to formulate statistical models, variable selection addressing validity concerns. Articulated the strengths and limitations of analytical approaches in the exploration of breast cancer screening data.• Provided statistical expertise in the development of protocols, grant applications, and scientific publications, generated regular reports and presented analysis results using Power BI to study team and external stakeholders, in support of fundraising efforts.• Breast Cancer Adjuvant Chemotherapy Wait Time Analysis:• Examined case report forms (CRF) to verify the alignment with the study protocol, ensuring consistency and adequacy in capturing data to fulfill research goals.• Generated descriptive statistics on time interval from surgery to initiation of adjuvant chemotherapy among breast cancer patients, modeling the impact of delayed adjuvant chemotherapy on patient outcomes.• Developed a Statistical Analysis Plan (SAP) for the research project collaboratively with the PI and clinical researchers, utilizing R to execute established statistical methods to analyze clinical data, and interpret the results.• Prospective Patient-Reported Outcomes Collection for Breast Cancer: Quality Assurance Insights: • Developed and produced analytical outputs that support surgical oncologist to evaluate Patient-Reported Outcomes (PRO) after breast cancer surgery, assessing the feasibility of PRO’s collection for future use at an institutional and regional level.

• Cleaned and merged three datasets (breast screening outcomes, AI inference and image catalog) for evaluating the performance of a commercial AI system for breast cancer detection using over 150k records using R and SQL.• Designed and performed stratified ROC analysis to estimate bias in the AI algorithm for different sub populations and to compare the AI prediction results to radiologist. Provided performance feedback to the AI company to help improve AI algorithm accuracy for cancer detection.