I am the Group Lead of the Biostatistics and Biomedical Informatics Group in the Computing and Computational Sciences Directorate at Oak Ridge National Laboratory. My training and experience is in the fields of demography, statistics, biomedical informatics, -omics, and life course epidemiology allow me to bring a unique set of expertise to building computational tools to identify populations at high risk for disease. I am currently the lead on DOE-National Cancer Institute (NCI) Modeling Outcomes using Surveillance Data and Scalable Artificial Intelligence (MOSSAIC) program, focused on advancing computing, predictive machine learning/deep learning (ML/DL) models, and large language models for near real time extraction of information from health records for NCI-supported cancer research.
My research has utilized data science and large, multimodal population level datasets to: 1) investigate temporal patterns in cancer risk and aging; 2) explore familial clustering of disease; 3) examine environmental determinants of health; 4) extract phenotypic information from population scale electronic health records; 5) develop new techniques to investigate the relationship between the environment and health; and 6) develop new methods for identifying patterns of multi-phenotype clustering in families. My research emphases include understanding how genetic and environmental influences throughout the life course affect disease risk, population health, and the health of future generations.
Adjunct Assistant Professor, Department of Population Sciences, University of Utah
Adjunct Assistant Professor, Department of Surgery, University of Utah
Adjunct Assistant Professor, Sociology, University of Utah
2022 NCI Director’s Award – Division of Cancer Control and Population Sciences, Cancer Surveillance Data Collaborators, for Data Science