Recognizing the pivotal role of behavior in gauging well-being, my research focuses on advancing digital phenotyping using smartphone data. While current practices heavily rely on wearable sensors, my approach expands the scope by integrating not just sensor data but also communication patterns and smartphone usage habits to predict stress levels using deep learning. By bridging this gap, my work aims to significantly enhance digital phenotyping accuracy for mental wellness assessments. This innovation could revolutionize healthcare by enabling more targeted interventions, directing attention specifically to individuals experiencing heightened stress levels.

I decided to participate in SuperUROP due to the near-autonomy that it provides, a departure from my previous UROP experiences. The additional responsibility that comes with doing a SuperUROP is helping me to not only gauge my long-term interest in research but also develop my communication skillset as I prepare to embark on my Master of Engineering degree.