SEED Grant Program 2022-2023

We’re extending the deadline for Seed Grants/Study Groups to July 15th 2022.

We want to thank those of you whom have already submitted and/or are planning to submit your paper this evening for all the work you’ve done thus far.

Responding to several requests for extensions, we have decided to extend the deadline for everyone. We understand that this time of year is for recuperation after a long hard year, and we’d like to give you all the opportunity to submit a proposal and hopefully get to work with you in the coming year.

If you have already submitted your application, please feel free to revise and resubmit by the new deadline.

Please submit your application here and email DISC to confirm your application has been received (DISC@tufts.edu). 


Overview: Data sciences can enable transformative research in many disciplines. However, initiating such work can be daunting, and knowing where to find partners and expertise may be difficult. The Data Intensive Studies Center (DISC) will help Tufts researchers overcome these barriers. Through this effort we seek to accelerate well defined efforts that combine the promise of disciplinary science breakthroughs enabled by new or existing data science methodologies to target high impact research and, in appropriate areas, large sponsored research efforts.


DISC will support such efforts with a seed grant of up to $25,000 and access to data scientists, data sets and other expertise at DISC. Projects should initiate a new group effort or provide new direction and significant enhancement to an existing group’s work. Primary review criteria will be:


- potential impact of the research proposed; 
- necessity of existing or new data science methodologies to enable the proposed research;
- qualifications of the team for conducting the research proposed;
- potential for follow-on scholarship and funding resulting from the preliminary work -proposed here;
- educational efforts enabled;
- support for diversity and broadening participation in data sciences.

 

Application Process: We seek applications including a cover page, up to 2-page project description and NSF or NIH style biosketches of the primary investigators. 


Cover Page:
- Title of group and a short abstract (elevator pitch!);
- Names and affiliations of all team members (no funding available for non-Tufts partners except costs incurred in collaboration e.g. travel to Tufts);
- Direct cost budget and brief justification.

Project Description:

  - Potential impact of project and challenges to be addressed; 

- Description of project workflow including existing state, planned work and clearly outlined deliverables for the seed investment and potential outcomes in longer term effort that may follow;

 - Potential follow on efforts and clearly identified future sponsors; 
 

 - Education efforts supported;
 

 - Plan for broadening participation.

 

Applications will be reviewed by faculty and DISC data scientists.

 

Study Groups:

Overview: Data sciences can enable transformative research in many disciplines. However, initiating such work can be difficult and needs partners and expertise that may be difficult to find. The scope, challenges and outcomes are often poorly defined without active and engaged partnerships among disciplinary and data scientists. 

The Data Intensive Studies Center (DISC) will help Tufts researchers overcome these barriers and facilitate the formation of groups to explore and define new high impact areas for scholarship enabled by data sciences.
In this first round the DISC will support such efforts with support of up to $10,000 that may be used for supporting group activities like external speakers and student time for activities such as data collection and literature survey. Projects should initiate a new group effort in a broad area. Groups will be led by disciplinary researchers and closely supported by DISC staff and fellows. Groups will be encouraged to define “sandbox” like structure for exploring preliminary concepts using new methodologies like machine learning with support from DISC staff.


Primary review criteria will be:
- Potential impact of the research proposed; 
- Necessity of existing or new data science methodologies to enable the proposed research;
- Qualifications of the team for conducting the research proposed;
- Potential for follow-on scholarship and funding resulting from the preliminary work proposed here;
- Impact beyond the primary domain.

 

Application Process: We seek 2-page applications including a cover page and NSF or NIH style biosketches of the PI. 


Cover Page:
- Title of group and a short abstract (elevator pitch!);
- Names and affiliations of all team members (no funding available for non-Tufts partners except costs incurred in collaboration e.g. travel to Tufts);
- Direct cost budget and brief justification.

Project Description:
 - Description of area of research and potential outcomes in longer term effort that may follow;
- Potential follow on efforts and clearly identified future sponsors.

Eligibility:  Tufts university faculty with eligibility to be PI on externally funded grants.

 

Please submit your application here and email DISC to confirm your application has been received (DISC@tufts.edu). 

 

FY2021 SEED Grant Winners

Predicting Dialogue Acts from Annotated Conversational Corpora

Team Lead: JP de Ruiter (SoE)

Team Member: Charles Threlkeld (SoE)

 

Establishing a Data-Based Classification Criterion for Deep Eutectic Solvents

Team Lead: Prof. Matthew Panzer (SoE)

 

Enhancing Security, Utility, Access, and Impact of Big Data on Youth Voting in America

Team Leads: Dr. Nancy Thomas and Dr. Kei Kawashima-Ginsberg (TISCH)

Team Members: Prabhat Gautam, Dr. Kelly Siegel-Stechler, and Sarah Keese (TISCH)

 

Unsupervised Clustering for Molecular Dynamics: Internal Distance Geometry and Manifold Dictionary Learning

Team Leads: Prof. Yu-Shan Lin and Prof. James M. Murphy (A&S)

 

Deep-learning enhanced multi-parametric quantitative ultrasound and photoacoustic imaging for early prediction of treatment response and recurrence

Team Lead: Dr. Srivalleesha Mallidi (SoE)

 

Systematic Inference Of Viral Phenotype Through AI Approaches

Team Lead: Jonathan Runstadler (Cummings)

Team Members: Martin Allen (SoE) and Laura Borkenhagen (Cummings)

 

Parsing single cell transcriptomics with tissue localization and neurological behavior

Team Lead: Shruti Sharma (TSUM)

Team Members: Katherine Sulka (TSUM), Jacob Hopkins (TSUM), Machlan Sawden (TSUM), Albert Tai (TSUM), and Eric Reed (DISC)

 

Agent-Based Computation and Data (ABCD)

Team Lead: Bruce M. Boghosian (A&S)

Team Members: Marc Hodes (SoE) and Peter J. Love (A&S)

 

Physics Guided Deep Learning for Actionable Outcomes

Team Lead: Shafiqul Islam (SoE)

Team Members: James Adler (A& S), Laurie Baise (SoE), Georgios Georgalis (DISC), Xiaozhe Hu (A&S), Eric Miller (SoE), Babak Moaveni (SoE)

 

Study Group for Spatial Multiomics

Team Leads: Michael T. Chin (TMC), Paola Sebastiani (TMC), and Albert Tai (TSUM)

Team Members: Rebecca Batorsky (CTSI), Heather Gardner (Cummings), Paul Mathew (TMC), Tanya Karagiannis (CTSI), Anastasia Gurinovich (TMC) and Eric Reed (DISC)

 

TUFTS University Art Collection HACK-A-THON Working Group

Team Lead: Dina Deitsch (SMFA)

Team Members: Anna Haensch (DISC), Laura McDonald (SMFA)

 

FY2020 Grant Winners