We drive cancer research through innovation and collaboration in the quantitative sciences. Our faculty and staff are expert biostatisticians, computational biologists and knowledge systems engineers, unified by a commitment to improving cancer care. We strive to understand the biology of cancer, test the efficacy of treatments and extract knowledge from complex datasets. We also lead the computational groups for several collaborative cancer research centers.
We are leaders in statistical methodology and clinical trial design. Our faculty provide expertise in all facets of clinical trial execution, including experimental design, logistics, and data analysis. We also analyze population and health systems data to better understand cancer risk and treatment efficacy.
We build computational tools to enable basic science discoveries and improve clinical approaches in cancer and biomedical research worldwide. These include methods for processing next-generation sequencing data, statistical packages for analyzing high-throughput data, and machine learning pipelines that integrate diverse, complex datasets.
We develop applied genomics and data sciences software to power cancer genomics research and precision cancer medicine. Our computational biologists, bioinformatics engineers, and software developers collaborate on high-impact projects in cancer genomics, immuno-oncology, clinical decision support, and data sharing.
Improving diagnosis and tailoring treatments are two of the most promising ways to improve cancer outcomes. New measurement technologies provide new sources of information that can be incorporated into current best practices. We use Machine Learning tools to help investigators design new data-driven approaches to diagnosis and treatment.