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Statistical Network Science and Smartphone-Based Digital Phenotyping

APRIL 5, 2019

Jukka-Pekka “JP” Onnela, D.Sc.
Associate Professor of Biostatistics
Director, Master’s Program in Health Data Science
Department of Biostatistics
Harvard T.H. Chan School of Public Health
Harvard University

About the Presentation: Behavior has traditionally been a difficult phenotype to characterize because of its temporal nature and context dependence. Traditionally it has been captured using either self-reported accounts of behavior or clinician-administered instruments or examinations. Both are subjective, qualitative, and typically cross-sectional; the latter is also confined to clinic / office settings. The ubiquity and capability of smartphones to collect social, behavioral, and cognitive data can be used to characterize psychopathology using objective measurement. We have previously defined digital phenotyping as the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, in particular smartphones. I will discuss digital phenotyping and will introduce Beiwe, our open source research platform for high-throughput, smartphone-based digital phenotyping. I will show how we have applied Beiwe in the mental health setting and will highlight some recent results. I will also consider some of the statistical and computational challenges that arise in this line of research.

About the Presenter:  JP Onnela is Associate Professor of Biostatistics in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health at Harvard University. He is also the Director of the Master’s Program in Health Data Science, one of the three data science programs at Harvard. He obtained his doctorate in network science in physics in Finland, and prior to starting his faculty position at Harvard in 2011, he completed a junior research fellowship at the University of Oxford, a Fulbright scholarship at Harvard Kennedy School, and a postdoctoral fellowship at Harvard Medical School. His main interest is in developing quantitative methods in two areas: statistical network science, the study of network representations of social and biological systems, and digital phenotyping, the moment-by-moment quantification of the individual-level human phenotype using smartphone data. He received NIH Director’s New Innovator Award in 2013.