Seed Funding for Methodologists Grant


Value and Duration
CDN $10,000 for up to 8 months
Application Deadline
May 31, 2024 23:59ET


The Data Sciences Institute (DSI) is a central hub and incubator for data science research, training, and partnerships at the University of Toronto. Its goal is to accelerate the impact of data sciences across disciplines to address pressing societal questions and to drive positive social change. The DSI Seed Funding for Methodologists initiative supports single applicants working in data sciences methodology or theory. In applying for this grant, applicants agree to (a) present their work to an audience of applied researchers and (b) apply for a Catalyst Grant with a new Collaborative Research Team (CRT). Ideal candidates will have a novel methodological or theoretical tool that has potential uses in a variety of applications. The purpose of this grant is to catalyse new Collaborative Research Teams by encouraging new collaborations of data science methodologists and theorists with applied researchers. By presenting and bringing to the fore innovative methodological and theoretical work, our goal is to spotlight exciting methodological innovations and facilitate new and unexpected connections between data science methodologists and applied researchers to foment cutting edge data science work. An applicant’s research area should focus on data sciences methodology or theory with the potential to be relevant to applied fields. Applicants should summarize their innovative data sciences work and explain its relevance and potential for engaging applied fields. If successful, applicants will present their work and funds of up to $10,000 can be used to seed a new Collaborative Research Team with the aim of applying for a DSI Catalyst Grant. Funds can be used for up to eight months to support that team through the application process. The DSI will fund five applicants each year and will hold calls twice yearly until our funding is used. Successful applicants are required to:
  1. Present their research and methodology/theory at a seminar, including its potential for applied fields. (The logistics to be supported by the DSI Office.) 
  2. Engage co-PIs to develop a DSI Catalyst Grant application with a new collaborative research team. 
In addition, awardees may be called upon to act as reviewers for future DSI awards competitions. The DSI is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas.

How to Apply

The award is open to applicants who meet the following criteria:     

*Faculty budgetary appointments for the University of Toronto are continuing, full-time academic appointments with salary commitments from a University of Toronto academic unit. 

Applications are submitted via the DSI Good Grants application portal.

Register an account and select “Start Application” for “Seed Funding for Methodologists Grant.”

DSI Good Grants Dashboard

The application is divided into tabs; each tab includes a set of instructions and fields to fill out. These instructions are also highlighted below.

Applicants will need to complete the following fields.

Tab 1: Start Here

  • Project Title
Tab 2: Applicant Information You will need the following information:
  • Name
  • Email
  • Institution
  • Division (if applicable)
  • Unit (if applicable)
Tab 3: Proposal

Methodology (maximum 500 words): Please summarize your novel methodological tool or approach.

Methods (maximum 500 words): Please summarize the potential that your tool or approach has for engaging applied fields.

Figures & Supporting Material (maximum 1 page): upload a 1-page .pdf with figures and supporting material.

Unit Head Signatures: Please fill out the provided template, convert to .pdf, and upload the unit head signature for the PI.

Tab 4: CV

Using the provided template, upload the PI's CV.

Once the applicant has submitted their component of the application on or before January 26, the following will occur:

Demographic Survey

The applicant or the person submitting on their behalf will receive a confirmation email that includes a link to a Demographic Survey. While this survey is required, when filling it out respondents have the option to select “Prefer not to answer” for all questions. The applicant has until February 2 to fill out the survey.

The DSI will form a Review Committee to lead the review of all eligible proposals received by the submission deadline. Reviewers are asked to consider the following categories:

  • The novelty of the proposed tool.
  • The potential that the tool has for engaging applied fields.

All application materials can be submitted directly onto the form. Certain fields on the form ask for uploads and require the following templates:

Past Recipients

Shehroz Khan (Toronto Rehabilitation Institute (KITE), University Health Network): “Predicting Future Anomalous Events”

Kuan Liu (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto): “Statistical Methods for Causal Inference and Longitudinal Trajectory Modelling with Multiple Features in Dementia”

Seyed Mohamad Moosavi (Department of Chemical Engineering and Applied Chemistry, Faculty of Applied Science and Engineering, University of Toronto): “Uncertainty estimation for machine learning using topological data analysis”

Jessica Gronsbell (Department of Statistical Sciences, Faculty of Arts and Science): “Infairness: Algorithmic bias evaluation and mitigation for large unlabeled datasets with broad application”

Joseph Jay Williams (Department of Computer Science, Faculty of Arts and Science): “SMART Systems: Dynamic self-optimizing system based on user input for time-sensitive applications”

Ting Kam Leonard Wong (Department of Computer and Mathematical Sciences, University of Toronto Scarborough): “Macroscopic Models of Equity Markets and Portfolio Selection”

Murat Erdogdu (Department of Computer Science, Faculty of Arts & Science): “Applications of Stein’s Method in ML”

Aya Mitani (Dalla Lana School of Public Health): “Matrix-Variate Regression for Multilevel Data”

Linbo Wang (Department of Computer & Mathematical Sciences, University of Toronto Scarborough): “Causal Inference: From Prediction to Actionable Insights”

Further Information

For more information, please contact