Lifespan AI

Time: 2023 - 2027
Funder: DFG
Funder description:

Research Unit (FOR 5347)

Contact person: Tanja Schultz

The work program consists of six projects grouped into three themes that pursue the Lifespan AI vision from different perspectives: Data and Methods (D), Models and Interpretation (M), and Inference and Causality (C). D1 will advance DL strategies to explore and process long-term temporal change based on integration of high-dimensional data from multiple sources; D2 will combine neural networks and mixed-effects models to predict individual health trajectories over the life course; M1 will develop “normalizing flow” methods to derive joint distributions and conditional densities for health data; M2 will create a cognitive digital twin from everyday human activities to predict change across age groups; C1 will develop time-adaptive, explainable AI methods for recurrent neural networks and event times; and C2 will derive a framework for “causal discovery” in longitudinal studies, combining different data sets and accounting for nonlinearities.