Funding Opportunities

The DSI Seed Funds Program supports research collaborations between data scientists and domain experts.

Proposal Deadline: Wednesday, November 8, 2023

Apply Here

Program Goals

The DSI Seed Funds Program supports new collaborations with the goal of developing longer term and deeper interdisciplinary relationships among faculty at Columbia. The program aims to advance research that combines data science expertise with domain expertise.

Proposals should represent new collaborations, which have the potential to lead to future funding opportunities with government, industry, or foundations. DSI Seed Funds should be viewed as planning grants for upcoming solicitations from DARPA, NIH, NSF, and others.

Special consideration will be given to proposals that take into account the responsible use of data to benefit society.

Funding and Terms

DSI provides two levels of funding. Proposals should indicate which level of funding is being requested.

Funding Level	Details
$25,000 (Maximum of 2 Years)	Intended for projects where significant salary support of Ph.D. students, postdoctoral researchers, or research scientists is not needed.
$75,000 (Maximum of 2 Years)	Proposed budget may include salary expenses for research support staff.

Seed Grant Terms (Both Levels)

Awardees will be required to submit quarterly financial reviews and biannual progress reports.
Eligibility for Year 2 continued funding is determined after review of the first year progress report and a review of effective project expenditures to date.
Progress reports must include details on external funding proposal submission(s) and other related activities (presentations,publications, etc.).
Projects that are awarded a second year of funding will be required to present their research findings to the Data Science Institute.

Avenues for Collaboration

Please be aware that DSI Research Scientists and Scholars may be available to collaborate on your proposal. Please reach out to them directly if this is of interest.

DSI Research Scientists and Scholars represent a wide range of expertise, from the foundations of data science to domains where data science is heavily used. Collaborating with a DSI research scientist or scholar may accelerate your research project.

Another avenue for potential collaborators is the Columbia Bridge to PhD Program in STEM

The Bridge to the Ph.D. Program in STEM is a structured, post-baccalaureate opportunity aimed to diversify the STEM professoriate and workforce. By including one of their scholars as part of your DSI Seed Funds research proposal, you contribute towards increasing pathways for underrepresented students to advance in STEM disciplines. The Office of the Vice Provost for Faculty Advancement covers 70% of the scholar’s salary and fringe, with 30% (~$17K) expected from the sponsoring principal investigator (PI). Your DSI Seed Funds budget is eligible to cover the PI’s expected cost for sponsoring a scholar.

Criteria for Proposal

Seed fund determinations will be assessed based on the criteria below. Please consider addressing these questions in your proposal.

Why is the proposed project novel? Additionally, describe the novelty of the collaboration in terms of people, disciplines, and/or schools. Contrast to prior work is recommended.
Why is seed funding essential to the success of this project?
How is the project necessarily inter-/multi-disciplinary?
What is the intended follow-up for this project to obtain future funds, especially plans to submit to large-scale funding opportunities?

All projects must be relevant to advancing and/or applying data science as a field.

Questions can be directed to dsi-seed@columbia.edu; or Radhika Patel, Chief Operating Officer at The Data Science Institute.

Apply Here

Recent Seed Fund Projects

Peter Bearman, Interdisciplinary Center for Innovative Theories and Empirics (INCITE), Graduate School of Arts and Sciences

Mark Olfson, Psychiatry and Epidemiology, Columbia University Medical Center

This project aims to use computational and machine learning methods to expand and demonstrate the efficacy of a novel data structure that captures at a granular level current inequalities in access to mental health treatment in the U.S., and to examine the impact of these inequalities on suicide—a leading cause of death and suffering in our society.
Yvon L. Woappi, Physiology & Cellular Biophysics, Dermatology, Columbia University Medical Center; and Biomedical Engineering, Columbia Engineering

Bianca Dumitrascu, Statistics, Graduate School of Arts and Sciences; and Irving Institute for Cancer Dynamics (IICD)

The complex cellular events necessary to achieve mammalian tissue regeneration remain unknown. Our research pairs machine learning-powered gene target identification with high-throughput interventional functional genomics to pinpoint the causal genetic and molecular combinatorial changes necessary to promote wound regeneration.
Hod Lipson, Mechanical Engineering, Columbia Engineering

Simon Billinge, Materials Science and Applied Physics and Applied Mathematics, Columbia Engineering

This project will explore the use of deep generative networks to automatically determine the structure of complex molecules, directly from x-ray powder diffraction images. The project will search for an end-to-end deep network that will be able to determine the full three-dimensional electron density field (i.e. the “shape” of the molecule), directly from a 1-dimensional diffraction strip. A variety of ML model architectures will be explored and applied to synthetic data generated by simulated powder diffraction experiments on relatively simple molecule groups. The project will specifically focus on Powder Crystallography, because while it is a much more difficult problem than solid crystallography, it can be applied to a broad range of materials and applications. This challenge is as significant as the protein-folding problem.
Jason Healey, Saltzman Institute of War and Peace Studies, School of International and Public Affairs (SIPA)

Savannah Thais, Data Science Institute

The SIPA CYsyphus “SIGH-si-fis” Cyber Recommendations Project is a decision-support tool that does the heavy lifting required to mine existing cyber reports and the expertise of the cybersecurity community. The project is using data science and machine learning to create a searchable database of recommendations to reduce by an order of magnitude the time needed to research and propose cyber policy decisions. The broader research has included collaboration from Jennifer E. Lake, University of Texas in Austin.
Kaveri A. Thakoor, Ophthalmology, Vagelos College of Physicians & Surgeons

Steven K. Feiner, Computer Science, Columbia Engineering

This DSI seed project aims to combine the pattern-recognition power of AI with the domain expertise of human medical experts to engineer human-vision–informed AI systems for enhanced eye disease detection accuracy and interpretability. We are one of the first teams that seeks to train AI systems with the eye movements of experts as they view ophthalmic images during disease diagnosis in order to create more trustworthy and accurate AI systems. The resulting systems could expedite disease detection, aid in medical education, and offer the potential to discover novel ocular diagnostic features.

Other Recently Funded Programs

The Columbia-IBM Center for Blockchain and Data Transparency supports research that advances innovation in blockchain, data transparency, data sharing, fair use of data, and related technologies for the good of society. The Center has funded several research projects to develop thought leadership and influence policy.

For an overview of current and past research projects, visit the Columbia-IBM Center for Blockchain and Data Transparency here.
The Data Science and Health Initiative (DASHI) is a partnership between the Data Science Institute and Columbia University Irving Medical Center to build collaborative research projects that leverage foundational data science for new clinical advances.

Three projects were awarded in 2022. Learn more about these projects here.