Application of state-of-the art biostatistical approaches and methods to advancing clinical research, as well as development of new methodologies for clinical trials and related areas. This includes Phase I, II, and III clinical trials as well as other studies conducted in the clinical arena. Some areas of application include cancer, cardiovascular disease, surgery and pediatrics. Areas of methodological development include issues of trial design and analysis of time-to-event (survival) data, among others.
Statistical Genetics and Genomics
Novel statistical methodology development and advanced applications of driven by rapidly evolving and high-dimensional data arising from modern high-throughput biological experiments. This includes such areas as quantitative trait loci study designs, expression and transcription factor analyses, personalized medicine, with special emphasis on experiments arising from next generation sequence technology and associations with human phenotypes.
Development and application of machine learning, algorithmic, and network approaches to problems in genetics and genomics, computational biology, cellular regulation, and cellular function.
Novel statistical and machine learning methodology development and applications aimed at descriptive and comparative analysis of medical images; and use of images in diagnosis, prognostication and prediction, and novel study design. Areas of application include cognitive decline, Alzheimer’s disease, affective disorders, and autism.
Application of theory and methods in computer sciences, including machine learning, natural language processing, and database models to advancement of quantitative methodology for clinical research and / or improved processes for clinical care.
Population Health Biostatistics
Novel biostatistical methodology development and applications in epidemiology, population and clinical observational studies of human health, and health services and health outcomes research. Classic areas of methodological inquiry include errors in measurement, missing data, causal inference, spatial data analysis, longitudinal data analysis, and supervised and unsupervised learning, among others.