Prof. Dr. Werner Brannath

FB 03: Mathematics and Computer Science
Applied Statistics and Biometry
Head of Biometry and member of collegial leadership of the Competence Center for Clinical Trials (KKSB); member of the Institute for Statistics (Faculty 3).

COMPETENCIES
Applied Statistics: Statistical aspects of study design and design of experiments, statistical modelling and data analyses.
Statistical Methodological Research: Sequential and adaptive designs, multiple testing and simultaneous confidence intervals, machine learning and variable selection, post-selection inference.

SHORT PROFILE
Werner Brannath has studied mathematics and physics at the Universities of Karlsruhe and Vienna. He was Research Assistant at the Department of Statistics at the University of Vienne and Assistant Professor at the Institute of Medical Statistics of the Medical Faculty/Medical University of Vienna (MUW). After his habilitation he became Associated Professor at the MUW. In 2001 he received an Erwin Schrödinger scholarship for a one year research stay at the Department of Statistics at Stanford University. Since August 2010 he is Professor of Applied Statistics and Biometry at the University of Bremen (Faculty 3) and head of the Biometry Section of the Competence Center for Clinical Trials Bremen (KKSB). He is also a member of the KKSB’s collegial leadership. In March 2017 he was elected as president of the German Region of the International Biometric Society (IBS-DR).

Besides his activities as responsible biostatistician (responsible for the biostatistics and data management) in a number of medical and health science research projects, he is developing and investigating statistical methods e.g. for sequential und adaptive designs and experiments in the engineering sciences as well as for simultaneous statistical inference. In the last years he also did research on variable selection procedures and methods for the evaluation of machine learning predictors. In a current research project he works on methods for statistical inference after model- und variable selection.