Biostatistics

Biostatistics faculty specialize in the development and application of cutting-edge quantitative methods to advance research in cancer biology, treatment, and prevention. Many new diagnostic techniques are not measured well by standard techniques. To address this issue, our faculty develop statistical methodologies using modern techniques to evaluate test performance and synthesize information to provide more accurate and more timely diagnosis. Methods research by our faculty also includes survival analysis, non-parametric Bayesian inference, and multivariate count data random generation. Our faculty have authored many numerous algorithms and R-packages. They have developed techniques for accelerating the convergence of slow computational algorithms popular in statistics and machine learning. These include the expectation-maximization (EM), minorization-maximization (MM), and proximal gradient algorithms.