As a PhD candidate in Biostatistics at the University of Illinois at Chicago (UIC), I have developed a strong foundation in statistical methodology and its application to real-world challenges. With a passion for transforming complex data into actionable insights, my work spans across the fields of causal inference, statistical modeling, and the integration of data from randomized trials and observational studies.My research focuses on enhancing the generalizability of clinical trial findings to target populations, utilizing cutting-edge methods like propensity score weighting, sensitivity analysis, and causal mediation analysis. Additionally, I have contributed to the development of R packages for statistical analysis, automating data processes, and providing statistical support for lung health researchers, including studies related to Long COVID.With hands-on experience in both academia and industry, including an internship at Merck where I developed innovative statistical designs for clinical trials, I bring a unique perspective to the biostatistics field. As a collaborator, I value effective communication and the ability to translate complex statistical concepts into clear, actionable findings.Looking forward, I am eager to apply my expertise in biostatistics, data science, and causal inference in the pharmaceutical industry, with a focus on improving healthcare outcomes through advanced statistical methods.