POPNET Connects with Sune Lehmann

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Life2vec: Predicting personality, death, emigration, and other life-events from embeddings of registry data

Over the past decade, machine learning has revolutionised computers’ ability to analyze text through flexible computational models. Beyond text, emerging transformer-based architectures have shown promise as tools to make sense of a range of multi-variate sequences from protein-structures to weather-forecasts due to their structural similarity to written language. Another type of process which has a strong structural similarity to language is human lives. From one perspective, lives are simply sequences of events: We are born, we visit the pediatrician, we start school, we move to a new location, we get married, and so on. Here, we use this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on day-to-day event sequences. 

About Sune Lehmann

Sune is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark. He’s 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, Sune’s research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. He works on large-scale behavioral data and while his primary focus is on modeling complex networks, his research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on Twitter.