Jennifer Sinnott

Jennifer Sinnott is an Associate Professor of Statistics at The Ohio State University, affiliated with the Translational Data Analytics program. She holds a PhD from Harvard University (2012) and joined Ohio State in 2015. Her research focuses on developing statistical methods for genomic and high-dimensional medical data, particularly in cancer research. She collaborates with TopCap, an international prostate cancer research group, and has contributed to molecular signature development for prostate cancer risk stratification.

Key research areas include survival analysis, statistical genetics, and pathway-based risk prediction. Her work addresses challenges in genetic studies, such as integrating multi-platform genomic data and improving phenotyping via electronic health records. She has received the A. David Mazzone Career Development Award (2013) and NIH funding for her projects.

Recent studies explore clinical trial enrollment impacts on cancer survival, disparities in gynecologic cancer treatment, and NMOSD disease burden analysis through the CIRCLES cohort. Her methodologies address semi-competing risks and high-dimensional data challenges in oncology and autoimmune diseases.

Jennifer’s contributions also include kernel machine regression applications for genomic pathway analysis and improving prognostic accuracy in endometrial cancer staging. She actively investigates dietary patterns’ role in cancer risk and telomere length as a prognostic marker in colorectal cancer.