Ipek Ensari’s research implements mobile health (mHealth) and machine learning techniques for patient-generated health data (PGHD) with high complexity and temporality, to investigate disease characterization (“phenotyping”) and patient symptom self-management. To this end, she investigates 1) between-individual variability in chronic disease symptoms in response to self-management approaches, with a focus on physical activity, 2) longitudinal, reciprocal fluctuations in disease symptoms to find the right point of intervention at the individual- and disease-level, and 3) integration and summarization techniques for complex, temporal meaningful PGHD to improve their sense-making. She completed her Ph.D. in kinesiology at the University of Illinois at Urbana-Champaign.

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