Amber Young is a PhD Candidate in Biostatistics at UNC Chapel Hill (expected graduation August 2026), where her research bridges computational statistics, machine learning, clinical trial design, and translational oncology to advance the discovery and targeting of biomarkers in cancer. Working under the co-supervision of Drs. Naim Rashid and Didong Li, she develops semi-supervised learning methods for the discovery of prognostic patient biomarkers and designs biomarker-driven Bayesian adaptive clinical trials.
Amber’s dissertation focuses on molecular subtype discovery in pancreatic cancer, where she creates methods for identifying clinically relevant and replicable cancer subtypes via semi-supervised matrix factorization approaches. Her work combines rigorous statistical methodology with practical applications in precision oncology, directly informing treatment decisions and clinical trial design. Her technical expertise spans clinical study design, C++, machine learning, high-performance computing (HPC), and R programming.
After joining UNC in 2019, Amber was selected as a trainee on the prestigious National Cancer Institute’s (NCI) Cancer Genomics Training Grant program, where she trained for three years (2019-2022) in cutting-edge computational methods for cancer genomics research. This fellowship provided intensive training at the intersection of biostatistics and cancer biology, establishing her foundation for translational research.
Amber has diverse research experience across multiple collaborations. As a Graduate Research Assistant at UNC Gillings School of Global Public Health (2021-2024), she worked with the Center for AIDS Research (CFAR), applying machine learning and high-performance computing to infectious disease research. Earlier, she served as a Graduate Research Assistant for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) at the Collaborative Studies Coordinating Center (2019-2020), contributing to large-scale epidemiological research.
In Summer 2022, Amber completed a Statistics and Data Analytics internship at Eli Lilly and Company in Indianapolis, where she developed a model-assisted dose-finding design for pediatric patients incorporating PK/PD (pharmacokinetic/pharmacodynamic) data. This industry experience demonstrates her ability to translate statistical methods to pharmaceutical drug development.
Amber earned her BS in Mathematics and Statistics from Purdue University (2015-2019), where she also served as a Computer Science Teaching Assistant (2018-2019), teaching C programming and supporting undergraduate students in computational methods.
She is open to Statistician and Biostatistician roles and brings a unique combination of methodological expertise, translational research experience, and industry knowledge to advance precision medicine and clinical trial innovation.
Key Lab Publications
- Young*, Amber Marie, et al. “Differential Transcript Usage Analysis Incorporating Quantification Uncertainty Via Compositional Measurement Error Regression Modeling.” Biostatistics, vol. 25, no. 2, Oxford University Press, 2024, pp. 559–76. CompDTUReg (Biostatistics 2024)