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Nina Ziegenbein is a PhD student in the Department of Public Health at Aarhus University, specializing in Epidemiology. Her research focuses on understanding the relationship between metabolism and cardiovascular risk, particularly in Type 2 diabetes. She employs multimodal data sources, including whole-room indirect calorimetry, wearable sensors, ECG, and continuous glucose monitors, to identify metabolic phenotypes. Ziegenbein is passionate about interdisciplinary research, leveraging her background in Intelligent Systems and applied machine learning to bridge the gap between computer science, medicine, and psychology. Her work involves developing computational methods to enhance cardiovascular risk prediction and collaborating with clinical researchers across various institutions. She is committed to advancing health data science and interpreting complex health data to deliver actionable insights.
Department of Computer Science offers tracks in Software Efficiency, Cryptography, and Data Science.