Julie Tian is a researcher in the Computational Psychiatry Group within the University of Alberta’s Department of Psychiatry. Trained in Pharmacology and Computer Science at McGill University, and seasoned with industry experience deploying digital technologies at the Royal Bank of Canada, she bridges health data insights with machine learning. Her work harnesses streaming data from smartwatches, smart rings, and smartphones to predict health status and clinical trajectories in depression and anxiety. By combining machine learning with contextual survey sampling, Julie extracts actionable signatures of sleep and activity that help clinicians understand vulnerable patient groups. Beyond modelling, she examines the barriers and facilitators that determine whether streaming data can translate from lab to clinic. Using established health-quality indicator frameworks, Julie leads multi-stakeholder co-creation workshops, engaging various patients and clinicians, to design transparent data platforms and intuitive consumer interfaces that maximize trust, usability, and sustained engagement. Her interdisciplinary perspective positions her at the forefront of a rapidly evolving field where remote sensing and individualized care converge. Outside the lab, she mentors emerging student scientists and advocates for accessible, scalable digital mental health innovation.