I develop advanced statistical and machine learning methods for digital and precision medicine, primarily focusing on analyzing data from wearable devices in diabetes and physical activity.

One of my significant contributions within the field of diabetes is the introduction of "glucodensity," a functional representation of time series data. Glucodensity representation improves existing clinical markers used for diabetes diagnosis and monitoring by capturing more information about individual glucose homeostasis. Additionally, I have made advancements in statistical methodology, including the creation of the biclustering algorithm for complex data, which has proven instrumental in analyzing diverse datasets. Furthermore, I have developed the first algorithm for quantifying uncertainty in metric spaces.

As a researcher, I pride myself on my creativity and collaborative nature. I strive to maintain a comprehensive perspective on statistical, mathematical, and biomedical research, recognizing the interconnectedness of these domains. My ultimate goal is to develop robust data analysis tools that will shape the future of medicine. Currently, my research encompasses various areas, including uncertainty quantification, causal inference in digital medicine, and survival analysis models that leverage the power of neural networks. These specific research domains have been motivated by critical biological challenges and the importance of translating these findings into clinical applications in biomedicine.