Bio
Alina Peluso is a research scientist in Biostatistics in the Advanced Computing for Health Sciences Section, part of the Computational Sciences and Engineering Division at 91°µÍø (ORNL).
She received her B.S. and M.S. degree in Statistics from the University of Milan-Bicocca (Italy) and a Ph.D. in Statistics from Brunel University London (UK). Her Ph.D. work advances the methodology and the application of regression models with discrete response including approaches to model a binary response in a health policy evaluation framework, as well as flexible discrete Weibull-based regression models (zero inflated, generalized linear mixed and generalized additive models) for count response variables leading to various applications in many fields.
Prior to joining ORNL, she worked as a lecturer in Statistics at Brunel University London (UK), and a postdoctoral research associate within the school of Medicine at Imperial College London (UK) and at the Francis Crick institute (UK) where she applied machine-learning and statistical modeling to the analysis of omics data to enhance biomedical discoveries and to predict pathway dynamics for precision medicine.
Her current research interests include casual inference in longitudinal data, regression models for count data, environmental and disease epidemiology, computational methods for statistical genomics and bioinformatics, bayesian learning and spatio-temporal modeling.
At ORNL, her current work contributes to projects of national importance, such as:
- US National Cancer Institute (part of National Institute of Health)
- Utilizing uncertainty quantification (UQ) for clinical text modeling outcomes with surveillance data and scalable artificial intelligence in cancer research (MOSSAIC)
- US Department of Veterans Affairs (VA)
- Developing next-generation risk-prediction models for suicide and drug overdose to support the REACHVet and STORM clinical prevention programs in collaboration with the U.S. Veterans Health Administration (VHA)
- Investigating the relationship between short-term environmental exposures, specifically air pollution, atmospheric pressure and temperature, and the risk of suicide and overdose deaths among U.S. Veterans
- Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS)
- Conducting a geospatial analysis of birth outcomes and their associations with community-based factors in Washington, DC
- Analyzing the impact of COVID-19 on healthcare utilization among patients with chronic diseases, with an emphasis on contextual health determinants
- Examining the impacts of environmental exposures on health outcomes in the U.S. MWCCS (Multi-site Women’s Cohort Study) population
- Examining the relationship between obesity, the effectiveness of bariatric surgery, food security, and small-area economic deprivation in the Washington, D.C. area