Nina has a background in Intelligent Systems, focusing on applied machine learning, data science, and ethical AI. Her experience working on projects that bridged computer science, medicine, and psychology sparked her passion for interdisciplinary research in healthcare. As a pre-phd student, her work centers on understanding the complex interplay between metabolism and cardiovascular risk in type 2 diabetes. She processes multimodal data from sources such as the whole-room indirect calorimeter, wearable sensors, ECG, and continuous glucose monitors to identify metabolic phenotypes and develop methods for improving cardiovascular risk prediction.