Bio
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 causal inference with longitudinal data, regression modeling for count data, environmental and disease epidemiology, computational methods in statistical genomics and bioinformatics, and spatio-temporal modeling.
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 advanced risk predictive models for suicide and drug overdose to support REACH VET and STORM prevention programs with the Veterans Health Administration (VHA).
- Spatial Modeling: Analyzing county- and state-level suicide 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, and suicide and overdose deaths among U.S. Veterans.
- Pharmacoepidemiology: Assessing the impact of prescribed medications on suicide risk in U.S. Veterans, focusing on multiple risk periods during treatment and the compounded effects of multiple medications to improve clinical practices and patient safety.
- Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) [Active project]
- Geospatial Analysis of Birth Outcomes: Studying the impact of community-based factors on birth outcomes in Washington, D.C.
- COVID-19 & Chronic Diseases: Examining how COVID-19 impacted healthcare utilization for patients with chronic diseases, and the role of social and environmental determinants.
- Health differences in MWCCS: Investigating environmental and social factors that influence differences in health outcomes among participants in the Multi-Site HIV-Positive Women’s Cohort Study (MWCCS).
- Obesity, Surgery, and Social Factors: Analyzing the link between obesity, bariatric surgery outcomes, food security, and area deprivation in Washington, D.C.
- 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.