Alina Peluso

Research Scientist in Biostatistics

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 Oak Ridge National Laboratory (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)
    • Modeling Outcomes using Surveillance data and Scalable Artificial Intelligence for Cancer (MOSSAIC)
  • US Department of Veterans Affairs
    • Enhance suicide risk modeling as part of the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) program
  • Medstar Health and Georgetown University Center for Clinical and Translational Science
    • Geospatial analysis of birth outcomes and associations with community-based socio-economic and environmental determinants of health 
    • Impact of COVID-19 on chronic disease onset and progression and the role of social and environmental determinants of health