As a PhD student with a background in Intelligent Systems, I specialize in applied machine learning and health data science. My experience working on projects that bridged computer science, medicine, and psychology sparked my passion for interdisciplinary research in healthcare. My research centers on understanding the complex interplay between metabolism and cardiovascular risk in type 2 diabetes.
I am conducting research on the relationship between metabolism and cardiovascular risk in Type 2 diabetes. To achieve this, I analyze multimodal data from sources such as whole-room indirect calorimetry, wearable sensors, ECG, and continuous glucose monitors to identify metabolic phenotypes and develop methods for improving cardiovascular risk prediction.
My primary area of responsibility is analyzing and integrating multimodal health data, including whole-room indirect calorimetry, wearable sensors, ECG, and continuous glucose monitoring, to study metabolic phenotypes in Type 2 diabetes. I also contribute to the development of computational methods for improving cardiovascular risk prediction and collaborate with clinical and computational researchers across institutions.
