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

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54EB020405-01
Application #
8905651
Study Section
Special Emphasis Panel (ZRG1-BST-Z (52))
Project Start
Project End
Budget Start
2014-09-29
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$1,388,946
Indirect Cost
$488,038
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Diamond, Steven; Boyd, Stephen (2016) CVXPY: A Python-Embedded Modeling Language for Convex Optimization. J Mach Learn Res 17:
Uchida, Thomas K; Seth, Ajay; Pouya, Soha et al. (2016) Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running. PLoS One 11:e0163417
Grover, Aditya; Leskovec, Jure (2016) node2vec: Scalable Feature Learning for Networks. KDD 2016:855-864
Uchida, Thomas K; Hicks, Jennifer L; Dembia, Christopher L et al. (2016) Stretching Your Energetic Budget: How Tendon Compliance Affects the Metabolic Cost of Running. PLoS One 11:e0150378
PLOS ONE Staff (2016) Correction: Effects of Three Motivationally Targeted Mobile Device Applications on Initial Physical Activity and Sedentary Behavior Change in Midlife and Older Adults: A Randomized Trial. PLoS One 11:e0160113
Wulczyn, Ellery; West, Robert; Zia, Leila et al. (2016) Growing Wikipedia Across Languages via Recommendation. Proc Int World Wide Web Conf 2016:975-985
Mallory, Emily K; Zhang, Ce; Ré, Christopher et al. (2016) Large-scale extraction of gene interactions from full-text literature using DeepDive. Bioinformatics 32:106-13
King, Abby C; Winter, Sandra J; Sheats, Jylana L et al. (2016) Leveraging Citizen Science and Information Technology for Population Physical Activity Promotion. Transl J Am Coll Sports Med 1:30-44
King, Abby C; Hekler, Eric B; Grieco, Lauren A et al. (2016) Effects of Three Motivationally Targeted Mobile Device Applications on Initial Physical Activity and Sedentary Behavior Change in Midlife and Older Adults: A Randomized Trial. PLoS One 11:e0156370
Benson, Austin R; Gleich, David F; Leskovec, Jure (2016) Higher-order organization of complex networks. Science 353:163-6

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