Using sequences of life-events to predict human lives
Visiting speaker
Sune Lehmann
Technical University of Denmark
Past Talk
In-person
Thursday
Jul 11, 2024
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1:00 pm
EST
Virtual
177 Huntington Ave.
11th floor
Devon House
58 St Katharine's Way
London E1W 1LP, UK
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Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.
About the speaker
About the speaker
Sune is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark. I’m also a Professor of Social Data Science at the Center for Social Data Science (SODAS), University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, my research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. I work on large-scale behavioral data and while my primary focus is on modeling complex networks, my research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on twitter.
Sune is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark. I’m also a Professor of Social Data Science at the Center for Social Data Science (SODAS), University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, my research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. I work on large-scale behavioral data and while my primary focus is on modeling complex networks, my research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on twitter.