[Updates: This blog was updated 10/29/14 to include a University of Wisconsin BD2K grant with significant statistician involvement. Also updated on 11/17 to note involvement of statistician on a Harvard University BD2K grant, o,n 11/19 for statistician involvement on Stanford University BD2K award, on 11/20 for statistician involvement in University of Memphis BD2K award.]
NSF and NIH issued several press releases in the last couple months regarding high-profile solicitations to support administration initiatives. Statisticians were among the recipients. Here I profile a few of them, and one from the Moore Foundation, to highlight the work of statisticians. If I've missed any, please let me know.
The programs are:
- Big Data to Knowledge (BD2K), NIH
- Data Infrastructure Building Blocks (DIBBs), NSF
- Collaborative Research in Computational Neuroscience (CRCNS), NIH/NSF
- BRAIN Initiative, NSF & NIH
- Gordon and Betty Moore Foundation
NIH BD2K
The October 9 NIH press release, "
NIH invests almost $32 million to increase utility of biomedical research data," announced FY14 awardees of the NIH’s Big Data to Knowledge (BD2K) initiative, which, according to the press release, "is projected to have a total investment of nearly $656 million through 2020, pending available funds." JHU's Brian Caffo, Harvard's Rafael Irizarry, and University of Washington's Daniela Witten were awardees in the category of
Development of Big Data Courses and Open Educational Resources:
Big Data education for the masses: MOOCs, modules, & intelligent tutoring systems
Johns Hopkins University
PI: Brian Scott Caffo
Grant Number: 1R25EB020378-01
This award will fund development of two Massive Open Online Course series for training in neuroimaging and genomic Big Data analysis. For instructors, modular Big Data bio-statistical content will be developed. For students, an interactive, gamified learning environment will be integrated into the MOOC series using swirl, an intelligent tutoring system.
Abstract
Biomedical Data Science Online Curriculum on HarvardX
Harvard University
PI: Rafael Angel Irizarry
Grant Number: 1R25GM114818-01
This award will fund a Biomedical Data Science Open Online Training Curriculum produced in a collaborative partnership between the Departments of Biostatistics in the Harvard School of Public Health, Computer Science in the Harvard School of Engineering and Applied Sciences, Statistics in the Harvard Faculty of Arts and Sciences, and Harvard's Massively Open Online Course (MOOC) initiative, HarvardX.
Abstract
Summer Institute for Statistics of Big Data
The University of Washington
PIs: Ali Shojaie and Daniela Witten
Grant Number: 1R25EB020380-01
The Summer Institute for Statistics of Big Data (SISBID) program will consist of five 2.5-day in-person courses, or modules, taught at the University of Washington each July. The five modules encompass: (1) Accessing Biomedical Big Data; (2) Data Visualization; (3) Supervised Methods for Statistical Machine Learning; (4) Unsupervised Methods for Statistical Machine Learning; and (5) Reproducible Research for Biomedical Big Data. This program will provide biomedical researchers with the computational and statistical training needed in order to take advantage of Big Data, so that they can more effectively use it to understand human diseases and to improve human health.
Abstract
In the category of Training and Career Development in Biomedical Big Data, Donna Lynn Coffman of Penn State is an awardee:
Novel Methods to Identify Momentary Risk States for Stress & Physical Inactivity
Pennsylvania State University
PI: Donna Lynn Coffman
Grant Number: 1K01ES025437-01
Healthy behaviors such as physical activity can decrease the risk of cardiovascular disease, diabetes, and other adverse health outcomes. This project will develop and apply big data methods to promote health behavior change; these methods will have broad implications for public health, particularly for the development of adaptive, individualized, health-behavior interventions.
Abstract
In the category of Centers of Excellence for Big Data Computing, statisticians are part of the University of Wisconsin team receiving the award to establish the Center for Predictive Computational Phenotyping (CPCP). The statisticians or biostatisticians involved in CPCP and/or the award are Sunduz Kelez, Christina Kendziorski, Michael Newton, Paul Rathouz, Grace Wahba, and Ming Yuan.
The Center for Predictive Computational Phenotyping
The University of Wisconsin – Madison
PI: Mark W. Craven
Grant Number: 1U54AI117924-01
The Center for Predictive Computational Phenotyping aims to accelerate the impact of predictive modeling on clinical practice. The Center will focus on issues related to computational phenotyping and will produce disease prediction models from machine learning and statistical methods; these models will integrate data from electronic health records, images, molecular profiles and other datasets to predict patient risks for breast cancer, heart attacks and severe blood clots.
Also in the category of Centers of Excellence for Big Data Computing, Harvard Biostatistics Professor Tianxi Cai is part of this award,
Patient-Centered Information Commons
Harvard University Medical School
PI: Isaac S. Kohane
Grant Number: 1U54HG007963-01
Investigators at the Patient-Centered Information Commons will develop systems to combine genetic, environmental, imaging, behavioral, and clinical data on individual patients from multiple sources into integrated sets. Computing across thousands of such individuals, will enable more accurate classification of individual disease or disease risk, and facilitate greater precision in patient disease prevention and treatment strategies.
and Stanford Statistics and Biostatistics Professor Trevor Hastie on this award
The National Center for Mobility Data Integration to Insight (The Mobilize Center)
Stanford University
PI: Scott L. Delp
Grant Number: 1U54EB020405-01
The Mobilize Center is poised to provide access to mobility data for over ten million people. The center will develop and disseminate a range of novel data science tools, including modeling and analysis methods to predict and improve the outcomes of surgeries in children with cerebral palsy and gait pathology; to identify new approaches to optimize mobility in individuals with osteoarthritis, running injuries, and other movement impairments; and to discover methods that motivate overweight and obese individuals to exercise more and in ways that promote joint health.
and University of Michigan Statistics Professor Susan Murphy on this award
Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)
The University of Memphis
PI: Santosh Kumar
Grant Number: 1U54EB020404-01
Researchers at the Center of Excellence for Mobile Sensor Data-to-Knowledge will develop innovative tools to make it easier to gather, analyze and interpret data from mobile sensors. These tools will reduce the burden of complex chronic disorders on health and healthcare by enabling detection and prediction of person-specific disease risk factors ahead of the onset of adverse clinical events.The center will study two specific problems as test cases: reducing hospital readmissions for patients with congestive heart failure and preventing relapse in those who have quit smoking.
DIBBS
In a October 1 press release,
Laying the groundwork for data-driven science, announced "$31 million in new funding to support 17 innovative projects under the Data Infrastructure Building Blocks (DIBBs) program", which include Duke University's Jerry Reiter for
An Integrated System for Public/Private Access to Large-scale, Confidential Social Science Data.
CRCNS
Three statisticians were also
awarded grants as part of
Collaborative Research in Computational Neuroscience program:
Award Number: R01MH079504
PI Name: Urban, Nathan (with Robert Kass)
Title: Physiological and computational approaches to understanding neuronal synchronization
Funding Organization: NIH--NIMH
See this 9/26/14 CMU Press Release, Carnegie Mellon Team Awarded NSF Grant to Combine Biophysical and Statistical Models of Neuronal Computation
Award Number: 0904353
PI Name: Paninski, Liam
Title: Optical reconstruction of cortical connectivity
Funding Organization: NSF--CISE
Award Number: R01NS073118
PI Name: Eden, Uri
Title: Hitting the Spot: Optimizing Placement of Deep Brain Stimulation Electrodes
Funding Organization: NIH--NINDS
BRAIN
On August 18, NSF announced
$10.8 million in early concept grants for brain research as part of the BRAIN Initiative. Among the awardees were JHU's Carey Priebe and Duke University's Katherine Heller:
BRAIN EAGER: Discovery and characterization of neural circuitry from behavior, connectivity patterns and activity patterns
Award Number:1451081; Principal Investigator:Carey Priebe; Organization:Johns Hopkins University;NSF Organization:DBI Award Date:09/01/2014
BRAIN EAGER: Integrative Cross-Modal and Cross-Species Brain Models: Motivation and Reward
Award Number:1451017; Principal Investigator:Katherine Heller; Organization:Duke University;NSF Organization:IIS Award Date:09/01/2014
On September 30, NIH announced
$46 million for BRAIN Initiative research but I didn't see any statisticians in the
list of awardees.
Gordon and Betty Moore Foundation
On October 2, the Moore Foundation
announced awardees for $21 million in grants for their
Data-Driven Discovery Initiative. The University of Chicago's Matthew Stephens was
among the awardees.
For information on how statisticians will help advance administration initiatives, see this July 2014 Amstat News article,
Statistical Scientists Advance Federal Research Initiatives, which reports on these three whitepapers:
See other ASA Science Policy blog entries. For ASA science policy updates, follow @ASA_SciPol on Twitter.