Y (Rena) Yang

Yang (Rena) Yang is a postdoctoral researcher in the Consumption and Healthy Lifestyles Chair Group, Department of Social Sciences, at Wageningen University & Research (WUR). Her research sits at the intersection of AI/machine learning, behavioral science, and public health, examining how data science methods can be applied to design and evaluate interventions that support sustainable behavior change and healthier lifestyles at the population level. She is particularly interested in the decision logic of personalized and adaptive digital interventions: how individual-level data are translated into behavioral targets, how theory and data interact in that translation, and how such interventions can be built, reported, and evaluated so that their effects are attributable and their logic is auditable.

She holds a PhD and MSc in Public Health and Health Systems, an MBET in Business, Entrepreneurship and Technology, and a BEng in Computer Engineering. Her methodological range spans machine learning, deep learning, statistical modeling, and the secondary use of large administrative and longitudinal datasets, with applications in substance use, fall risk, dialysis care, and chronic disease.

Rena has led projects on digital disease surveillance, biomedical data translation, and predictive modeling, with research experience in Canada and the Netherlands. She previously co-led national initiatives on digital health and cancer prediction at the University of Waterloo and managed the NIH/NCATS Biomedical Data Translator project at Maastricht University. At WUR, she advances AI-driven health behavior analytics, develops methods for evaluating personalized and adaptive digital interventions, and teaches data science for health.

Her work has appeared in The Lancet Digital Health, The Lancet Regional Health - Americas, the Journal of Medical Internet Research, Clinical Nutrition, and International Journal of Mental Health and Addiction. With prior leadership roles at Siemens, Danaher, and Lenovo, Rena brings extensive industry experience to her academic work. Her research interests include machine learning for behavior change, theory-informed personalization, AI-assisted conversational agents for health behavior support, population health data science, precision and preventive health, data quality and reproducibility, ethical and explainable AI in health, and cross-cultural comparison of health systems.