My research focuses on developing statistical tools to address clinical research questions using genetic and genomic data and other high dimensional medical data sources, with a particular focus on methods relevant to cancer research. I have developed kernel machine regression methods that allow flexible use of genomic pathways in risk prediction models and in the semi-competing risks setting. I have also worked on methodological issues arising in modern genetic studies, such as those integrating multiple studies using multiple genotyping platforms, and those seeking to perform genetic association testing with imperfect phenotypes estimated using phenotyping algorithms accessing electronic health record data. The direction of my work has been influenced by my longterm close collaboration with TopCap, an international team of prostate cancer researchers, and with this group I have helped lead the development of molecular signatures to improve risk stratification of prostate cancer patients.

Jennifer Sinnott joined the statistics faculty in 2015. She is also affiliated faculty with Translational Data Analytics @ Ohio State.  She received the A. David Mazzone Career Development Award in 2013 supporting several prostate cancer research projects, and her research has also been funded by the NIH.