Seed Funds Program

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

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Proposal Deadline: November 6, 2024
PDF of Application Questions

Program Goals

The DSI Seed Funds Program aims to advance research that brings together data science and domain expertise in new collaborations between Columbia faculty. Our goal is to foster long-term and deep interdisciplinary research relationships across the University, and to drive promising research initiatives forward.

The Data Science Institute is currently inviting new proposals for the Seed Funds Program, focused on these University strategic priority areas:

Artificial Intelligence (AI)
Climate
Mental Health
Social Justice

This year, DSI will award four grants, each with a maximum award of $125,000 for use over the period of one year. Grants will be awarded in January 2025, with projects set to begin on July 1, 2025 and end on June 30, 2026.

Program Terms

Proposals should represent new collaborations between faculty who have not previously worked together.
Proposals should be grounded in research that has the potential to garner future funding from government, industry or foundations.
The Seed award term will be approximately 18 months. DSI will aim to issue project awards in early January. From there, PIs will have six months for project set-up for hiring, budgeting, planning, and securing access to necessary data sets.
Projects will run from July 1 – June 30 of the following year. The expectation is that the entire award amount will be expensed in this period. Any funds that remain after June 30, 2026 will revert to the Institute.
DSI Seed Funds should be viewed as planning grants for future solicitations from DARPA, NIH, NSF, and others. We encourage the awardees to submit external grants through the Institute.

All proposals should discuss responsible and ethical use of data.

Funding

Funding Amount	Up to $125,000
Awards Per Term	Up to four projects will be awarded per cohort
Funding Period	One year of funding will be awarded (July 1 – June 30)

Funding Guidelines

Funding may be used for, but is not limited to:
- Operating supplies
- Equipment
- Travel directly related to research
- Publication costs

Seed funds may be used to support the hiring of PhD students, postdoctoral researchers, and student assistants.

Restrictions

Program funding cannot be used for:
- Faculty salary support, including summer salary support
- Salary support for DSI Research Scientists. Although DSI Research Scientists can participate on proposal teams, their salary support is already covered by the Institute.
- Salary support for project partners external to Columbia University

No-Cost-Extension Policy: The Seed Funds program does not allow for no-cost extensions. All project funding that is not expensed by June 30 will revert to the Institute.

There will be a six-month project set-up period; projects that do not have the personnel identified in place by June 15 (two weeks before the official project start date of July 1) will not have funding released. In this instance, the DSI team will schedule a conversation to discuss the project’s viability and determine whether a delayed start time is feasible. The Institute reserves the right to rescind the award if appropriate plans and details are not provided by the PI regarding project delay.

Eligibility Requirements

Each proposal must have a minimum of two collaborators, who are Columbia University faculty members who can serve as a Principal Investigator according to the Columbia Policy.
PIs must be from two distinct departments within the University; applications that feature two faculty from the same department will not be accepted.
Faculty should not have previously collaborated together.
Postdoctoral researchers may not serve as a PI for this seed award.

Seed Grant Terms

Reporting	Awardees will be required to submit progress reports. – A six-month progress report will be required on January 15, 2025. The report must detail progress to date, and plans to seek additional funding based on your research. – A final report will be required on June 15. – DSI will ask awardees to share updates on any publications, grant/gift funding, and other evidence of progress for several years after the project has ended.
Research Presentations	Awardees must be willing to present their research at future DSI events or seminars, including presentations to groups such as the DSI Executive Committee and the Data and Society Council, among others.
DSI Community Engagement	Awardees may be asked to review future Seed Funds proposals as part of service to the Institute. Additionally, we encourage award recipients to actively engage with the broader DSI community. Consider the following opportunities to deepen your involvement: – Participate in the Capstone Program by submitting a research project for MSDS student participation. – Mentor an MSDS student. – Serve as a reviewer for DSI postdoctoral applications. -Volunteer for or participate in the annual Data Science Day, held each spring semester. – Join a DSI Research Center. – Provide research opportunities to students through the Campus Connections program.
DSI Acknowledgment	Awardees should acknowledge DSI’s support in any papers, publications, or reports resulting from award fund activities.
External Funding	The goal of the Data Science Institute Seeds Funds Program is to support new collaborations that will lead to longer term and deeper relationships among faculty in different disciplines across Columbia University. We expect that this seed money will provide a basis for your team to submit larger proposals, and, we ask that you submit external funding proposals and opportunities related to this research through the Data Science Institute.

Avenues for Collaboration

The Data Science Institute has a number of pathways to source additional support for your research:

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. Please reach out to them directly if this is of interest.
DSI Scholars Program: matches Columbia students with opportunities to engage in data science-related research projects led by Columbia faculty.
Campus Connections is another program that may be available to you if you find you need student research support after you have received an award.

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 a Bridge to the PhD candidate in 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.
How does this proposal align with one or more of the following priority areas:
- AI
- Climate
- Mental Health
- Social Justice
Why is seed funding essential to the success of this project?
How is the project inter-/multi-disciplinary?
Please outline your plans for securing future funding for this project, particularly your strategy for pursuing large-scale funding opportunities. Specifically, identify the external funding sources you plan to apply for and provide a detailed timeline for these applications.

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.

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.