Our data science research is tied to three Driving Biomedical Problems that we will use to focus, test, and validate the data science methods. These problems represent major opportunities to improve human mobility and health. We propose the following specific aims: 1. Data Science Cores: Develop and disseminate data science tools to overcome several of the major challenges in exploiting big data in biomedical research. In particular, we will: a. Develop robust, flexible, and automated optimization tools for generating personalized biomechanical models and simulations from diverse experimental movement data. b. Create techniques to make predictions and classifications and identify insightful correlations from large sets of noisy, sparse, and complex data, whether discrete or time-varying. c. Develop tools to model the role of behavioral and social dynamics in human health based on information collected with smartphones and wearable activity monitors. d. Establish machine learning systems that integrate diverse data sources and modeling approaches to aid clinical decision-making and transparently communicate with clinicians
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| Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure (2018) Modeling Interdependent and Periodic Real-World Action Sequences. Proc Int World Wide Web Conf 2018:803-812 |
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