The mission of Duke AI Health is to enable the discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards.
The 2022 AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. After seeing a strong response to the Proposal Studio concept and the following virtual learning experiences in 2021, AI Health plans to continue building on last year’s success with the overarching goal of fostering high-impact, rigorous, and competitive proposals for scientific awards.
Who Should Participate?
Clinical and non-clinical investigators from any department at Duke are invited to apply.
Our goal is to foster health data science across Duke. We will welcome participation from non-School of Medicine investigators, to build more collaborations between faculty from the Schools of Medicine and Nursing and campus faculty.
We hope to see participation by a mix of investigators representing various levels of experience. We especially encourage investigators who are at the assistant professor/early associate professor level.
What Types of Proposals?
We are interested in research that is aligned with the mission of AI Health at the intersection of data science and clinical/translational science, especially predictive modeling, machine learning, and comparative effectiveness research (CER). Topics of interest include:
- · Use of existing data and/or merging sources
- · Developing/applying advanced quantitative methods with big data
- · Research taking place within health systems, such as prospective use of predictive models
National Institutes of Health R21 (Exploratory/Developmental Research Grant) and R01 (Research Project Grant) proposals are examples of the types of applications that may be especially well-aligned with this initiative.
We would particularly like to support research teams who have results from small, internal data science projects (such as department-sponsored analyses or Duke Forge demonstration projects) that are ready to bridge into larger cohorts and populations.
What Are the Proposal Studios?
After a cohort of applications are accepted, each project will have the opportunity for written feedback and/or a scientific design workshop session to substantively engage with the specific aims and research strategy, especially significance, innovation, and approach.
We also expect that engagement with AI Health will be tailored to each proposal and may include facilitating introductions to potential collaborators and helping applicants connect with other resources for proposal development at Duke.
There is no charge to the investigator for participation in the Proposal Studios, but participants will be asked to confidentially share information about their proposal scores and outcome metrics so that the program can be strengthened over time.
You do not need to have a full research team to apply. We expect that the proposal studios can help “match-make,” including making introductions between potential clinical and quantitative collaborators.
How to Apply
Applications will consist of approximately 2 pages: 1 page as a scientific overview (draft summary and specific aims) and 1 page as a project overview (including team members, identified proposal opportunities, and specific requests).
To submit your application, please visit: https://duke.qualtrics.com/jfe/form/SV_6WE0ncKshPWTssC
The deadline for submitting applications is 10:00 PM Eastern time on Monday, March 28, 2022.
Fan Li, PhD, is a Professor at the Department of Statistical Science with a secondary appointment at the Department of Biostatistics and Bioinformatics at Duke University. Her main research interest is causal inference – designs and analysis for evaluating treatments and interventions in randomized experiments and observational studies, and their applications to health studies and computational social science. Dr. Li also works on the interface between causal inference and machine learning and has developed methods for propensity score, clinical trials, randomized experiments, difference-in-differences, regression discontinuity designs, representation learning, and Bayesian methods. She has also worked on statistical methods for missing data. In addition, she has done some work in Bayesian graphical modeling for genomics and neuroimaging data.
Maciej A. Mazurowski, PhD, is an Associate Professor of Radiology, Electrical and Computer Engineering, and Biostatistics and Bioinformatics at Duke University. He is also the Scientific Director of the Duke Center for Artificial Intelligence in Radiology, with a research focus on the development of machine learning algorithms and their application in medical imaging. His lab at Duke works on a wide range of applications, including breast cancer, brain cancer, thyroid cancer, musculoskeletal imaging and others. The lab’s technical work in algorithm development and evaluation includes 3D pyramid pooling in convolutional neural networks, style transfer for image harmonization, and class imbalance in deep learning. Dr. Mazurowski’s work has been sponsored by the National Institutes of Health, the U.S. Department of Defense, Radiological Society of North America, industry, and Duke internal competitive grants.