Seed Grant Programs

IICD-Data Science award, in partnership with the Data Science Institute

The DSI Seed Funds Program supports new collaborations that will lead to longer term and deeper relationships among faculty in different disciplines across campus. Aimed at advancing research that combines data science expertise with domain expertise, the program’s funded research should embody the spirit of the Institute’s mission statement.

The Data Science Institute and the Herbert and Florence Irving Institute for Cancer Dynamics are collaborating to support up to two seed grants, up to $100,000 annually, for a maximum of two years, that align with their shared mission of improving the understanding of cancer biology, origins, treatment and prevention through data-driven methods and processes. The Institute is particularly interested in candidates that may further advance core research in Statistical and Probabilistic Modeling. If you wish to have your Seed Fund application reviewed for this opportunity as well, please indicate as such on the application cover page (check box).

Statement on Racial Equity

The Data Science Institute is committed to racial equity and justice. Proposals should explicitly state that the project will uphold these values, e.g., stating that the methods used to collect and analyze project data and the project outcomes reported are fair, just, and ethical.

For more information and application:

Deadline for proposal submission is Monday, November 9, 2020, 3:00 p.m.

2019/2020 seed grant award

The Herbert and Florence Irving Institute (IICD) is pleased to announce the recipients of the 2019/2020 seed grant award in partnership with the Data Science Institute (DSI).        

DSI and IICD awarded two seeds grants that align with their shared mission of improving the understanding of cancer biology, origins, treatment and prevention through data-driven methods and processes. Each team will receive up to $100,000 for one year and be eligible for a second year of funding.

Project Title: Modeling the Dynamics of Young Onset Colorectal Cancer using Big Population Data

Investigators: Wan Yang, Mary Beth Terry, Jianhua Hu, Piero Dalerba

Using multiple nationally representative large-scale exposure and cancer incidence datasets, this project will build a novel model-inference system to study the dynamics of colorectal cancer, test a range of risk mechanisms over the life course, and identify key risk factors underlying the recent increase in young onset colorectal cancer incidence in the United States to support more effective early prevention.

Project Title: Probabilistic modeling of intercellular interactions that drive ferroptosis susceptibility of therapy-resistant cancer cells

Investigators: Elham Azizi, Jellert Gaublomme, Brent Stockwell

In this project, we leverage machine learning techniques to combine two types of single-cell data modalities with the goal of achieving a more comprehensive characterization of heterogeneous cell states in the tumor microenvironment. Specifically, we will develop probabilistic models to elucidate the role of intercellular interactions in driving susceptibility of treatment-resistant mesenchymal tumor cells to a newly discovered ferroptotic vulnerability, which could offer a therapeutic avenue to prevent survival of these cancer cells that are prone to metastasis.

This request for proposal is now closed. Please check again in September 2020 for 2020-21 proposal application details.