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

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-Z (52))
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
United States
Zip Code
Wu, Sen; Hsiao, Luke; Cheng, Xiao et al. (2018) Fonduer: Knowledge Base Construction from Richly Formatted Data. Proc ACM SIGMOD Int Conf Manag Data 2018:1301-1316
Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure (2018) Modeling Interdependent and Periodic Real-World Action Sequences. Proc Int World Wide Web Conf 2018:803-812
Erdemir, Ahmet; Hunter, Peter J; Holzapfel, Gerhard A et al. (2018) Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research. J Biomech Eng 140:
Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure et al. (2018) HUMAN DECISIONS AND MACHINE PREDICTIONS. Q J Econ 133:237-293
Pierson, Emma; Althoff, Tim; Leskovec, Jure (2018) Modeling Individual Cyclic Variation in Human Behavior. Proc Int World Wide Web Conf 2018:107-116
Powers, Scott; Qian, Junyang; Jung, Kenneth et al. (2018) Some methods for heterogeneous treatment effect estimation in high dimensions. Stat Med 37:1767-1787
Halilaj, Eni; Rajagopal, Apoorva; Fiterau, Madalina et al. (2018) Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J Biomech 81:1-11
Lin, Zhiyuan; Althoff, Tim; Leskovec, Jure (2018) I'll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application. Proc Int World Wide Web Conf 2018:1501-1511
Seth, Ajay; Hicks, Jennifer L; Uchida, Thomas K et al. (2018) OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 14:e1006223
Yong, Jennifer R; Silder, Amy; Montgomery, Kate L et al. (2018) Acute changes in foot strike pattern and cadence affect running parameters associated with tibial stress fractures. J Biomech 76:1-7

Showing the most recent 10 out of 78 publications