Alina Peluso

Alina 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 research associate within the school of Medicine at Imperial College London (UK) and at the Francis Crick institute (UK) where she applied 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 developing and applying predictive statistical models for integrating and analyzing clinical, observational, and public health data; advancing precision epidemiology and precision medicine by bridging population-scale evidence with individual-level data; and conducting spatial (individual- to population-level) and temporal (short- to long-term) analyses in biostatistics, bioinformatics, epidemiology, and health data analytics.

At ORNL, her current work contributes to projects of national importance, such as: 

• U.S. National Cancer Institute (NCI), NIH [Former project]
  • AI in Cancer Research: Applying uncertainty quantification (UQ) to clinical text modeling and surveillance data to enhance AI scalability and accuracy in cancer research (MOSSAIC project).
• U.S. Department of Veterans Affairs (VA) [Active project]
  • Risk Predictive Modeling: Developing and validating advanced risk predictive models for suicide and drug overdose to support the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) and the Stratification Tool for Opioid Risk Mitigation (STORM) prevention programs with the Veterans Health Administration (VHA).
  • Spatial Modeling: Analyzing county- and state-level suicide and overdose mortality patterns in VHA patients and the broader U.S. population using spatial epidemiology modeling.
  • Environmental Epidemiology: Investigating the link between short-term air pollution, weather conditions, atmospheric pressure and suicide and overdose deaths among U.S. Veterans.
  • Pharmacoepidemiology: Assessing the impact of prescribed medications initiation on suicide and overdose risk in U.S. Veterans to improve clinical practices and patient safety.
• Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) [Active project]
  • Community Determinants of Adverse Birth Outcomes: Examining how neighborhood, social, and community-level factors influence the risk of preterm birth and small-for-gestational-age births in Washington, D.C.
  • Community Determinants of Healthcare Utilization During COVID-19: Investigating how the COVID-19 pandemic affected healthcare utilization among individuals with chronic diseases and the role of social factors in shaping care access and outcomes.
  • Community Determinants of Health Outcomes in MWCCS: Examining how environmental exposures and social determinants contribute to differences in health outcomes among participants in the Multi-Site HIV-Positive Women’s Cohort Study (MWCCS).
  • Community Determinants of Obesity and Bariatric Surgery Outcomes: Investigating how food security, area deprivation, and other community-level factors influence obesity prevalence and bariatric surgery outcomes in Washington, D.C.
  • Community Determinants of COPD Outcomes: Examining how environmental exposures and community-level factors influence health outcomes among individuals with chronic obstructive pulmonary disease (COPD) in the the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) cohort.
  • Community Determinants of Colorectal Cancer Outcomes: Investigating how environmental, neighborhood, and socioeconomic factors—including access to greenspace and recreational spaces—influence physical activity, survival, and disparities in colorectal cancer outcomes across diverse populations in the Disparities and Cancer Epidemiology (DANCE) cohort.
• Intelligence Advanced Research Projects Activity (IARPA) Biometric Recognition & Identification at Altitude and Range (BRIAR) [Active project]
  • Biometric recognition system: Analyzing of advanced multimodal biometric systems that integrate face, body, and gait features to enhance recognition performance under diverse environmental and imaging conditions, supporting intelligence, defense, and national security operations.