NCI Data Jamboree (Project Abstract Submission): Submission #20

Presenter Information
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First Name: Jenny
Middle Initial: {Empty}
Last Name: Ozga
Degree(s): Ph.D.
Position/Title/Career Status: Senior Research Associate
Organization: Westat, Inc.
Organization Address:
Bethesda, MD

Email: jennyozga@westat.com

Additional Authors
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List of Additional Authors:
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Abstract Information
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Abstract Category: Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords: {Empty}
Abstract Title: Jenny Ozga, PhD Project Abstract Submission
Abstract:
I am a public health researcher with over 10 years of experience conducting behavioral epidemiology and tobacco regulatory science research using large-scale population health datasets. My work focuses on leveraging nationally representative surveys and longitudinal cohorts to better understand behavioral risk factors, disparities, and patterns of health-related behaviors. I have extensive experience analyzing data from NCI-supported studies, including the Population Assessment of Tobacco and Health (PATH) Study and the National Health Interview Survey (NHIS). My research has involved survey-weighted methods, regression modeling, and integration of multiple data domains, including behavioral measures, social and environmental determinants of health, and biomarker data. Through this work, I have developed expertise in addressing common challenges associated with complex health datasets, including missing data and developing documentation to support reproducible research.

I am interested in participating in the NCI Data Jamboree because it provides an opportunity to collaborate with researchers from diverse disciplines and expand my understanding of emerging approaches in biomedical data science. My current work relies heavily on large survey datasets, and I am interested in learning how artificial intelligence and machine learning approaches can complement traditional epidemiologic methods to address increasingly complex research questions. I am particularly interested in approaches that integrate multimodal data, including behavioral, social, environmental, and biological measures. 

Through participation in the jamboree, I hope to develop a stronger understanding of how AI/ML approaches can be applied to epidemiologic research questions and how diverse health data sources can be integrated to generate new insights. I hope to contribute my experience working with complex longitudinal and nationally representative datasets while gaining exposure to methods that extend beyond traditional analytic approaches. Ultimately, I hope to build expertise and establish new interdisciplinary collaborations that allow me to bridge behavioral epidemiology and emerging data science approaches.