TRIADS Seed Grant Program

2024 Seed Grant Program

The Transdisciplinary Institute in Applied Data Sciences (TRIADS) is excited to announce its 2024 Seed Grant Program.

A summary of the program is below. View the full announcement document for complete details.

Important Dates:

Proposal deadline: February 23, 2024
Awards announcement: March 29, 2024


The mission of TRIADS is to build and support teams for data-powered research, seeking to better understand vital societal issues.

To support this mission, the TRIADS Seed Grant Program aims to (1) quickly advance existing research teams, (2) develop projects in new transdisciplinary fields of inquiry, and (3) build communities of potential collaborators around shared questions, methods, and data.

Eligibility Criteria:

  • The primary PI(s) must be full-time faculty members at Washington University in St. Louis. 
  • The collaborative team must include faculty from multiple units (departments, schools, centers, etc.).
  • Collaborations that involve faculty from multiple schools are encouraged, but not required. 

Applications from all research traditions are invited. TRIADS is a big tent. Do not rule your work out.

You can see the winners of the TRIADS 2023 Seed Grant program below.

Review Process:

1.) Full proposals are due February 23. All applications will be reviewed internally by a committee comprised of TRIADS leadership, a committee of faculty from diverse fields and schools, and the Vice Dean of Research.

2.) The final funding decision will be communicated to the corresponding PI by March 29. Feedback on the application will be provided.


TRIADS will fund small ($2,000-$9,999), medium ($10,000-39,999), and large proposals (greater than $40,000). Typically, the maximum award will be $100,000 in direct costs for a one-year period, but we will consider awards over several years if a strong case can be made for the need.

Successful seed grant proposals will also receive:

  • Support from the TRIADS software engineering team
  • Access to our new collaborative space located on the 4th floor of Jolley Hall
  • Support from our Research Development Associate to identify external funding opportunities and prepare successful applications
  • Support in accessing the RIS computing cluster and data storage facilities
  • Server support for web applications, databases, or other online content
  • Publication costs for any resulting publications
  • Staff support for organizing conferences, summits, and other events.

Proposal Guidelines:

The proposal should be 3.5 pages long, not including the cover page or figures/tables. Please use Times New Roman, 11-point font, single-spaced and 1-inch page margins. All pages, except the cover page should be numbered consecutively throughout the application. 

To submit a proposal, visit our Seed Grants page in InfoReady:

If you experience any issues submitting through InfoReady, please contact Julie Rivinus at

The proposal must include the following:

1.) Cover page (Include the project title, and name, title, department, and e-mail address of each the PI(s) and Co-I(s); not included in the page limit). Principal Investigator(s) refer to the individual(s) who will be responsible for the scientific or technical direction of the project. If more than one, the first one listed will have primary responsibility for communications with TRIADS and the submission of reports. Co-I(s) does not have overall responsibility or spending authority as the PI. 

2.) Project summary

3.) Research plan

a. Specific objectives
b. Relevance to the TRIADS mission
c. Approach and workplan
d. Outcomes, including planned external funding proposals
e. Metrics for evaluating success
f. Potential to contribute to the diversity of the data science field

Other required documents (not included in the page limit):

1.) Budget (use the template located here). Please contact Bill Courtney ( for assistance in creating a budget.
2.) Budget justification (include the role/responsibilities/contributions of each PI)
3.) Timeline (include milestones) for completing the scope of the work
4.) 200-word description of the research team and their expected role in the research. Please include links to the PI(s) website. 
5.) Works Cited/References
6.) List of targeted grants (for each grant include funding agency, anticipated funding request, deadline, link to RFP). Please consult with Bhavna Hirani ( in advance of submission for help in identifying relevant programs.
7.) A 200-word mentoring and recruitment plan for postdoc (if requested). This should include indications for where the postdoc will be physically located on campus.
8.) A 200-word summary of any TRIADS staff support (e.g., conference planning/organizing, database management, software development, payroll support, subject recruitment, etc.) that is part of the request but not included in the budget.


Jacob Montgomery (, Tammy English ( or Bhavna Hirani ( for resources and support

2023 Seed Grant Recipients:

Accounting for Human Bias to Improve AI-Assisted Decision Making
Wouter Kool (Psychological & Brain Sciences), Chien-Ju Ho (Computer Science & Engineering)

Now more than ever, artificial intelligence assists and informs human decision making – from what to watch on YouTube to how to interpret crime data. But when AI systems make recommendations or provide information to humans, they also need to consider known biases in how people interpret and act on information. This project aims to develop an AI algorithm that incorporates knowledge about human biases to lead to better decision making. It will also study how people modify their behavior when they know that their decisions will inform AI systems.

Legacy of Neglect: Linking Flood Hazards, Pathogen Exposure, and Health Inequities in Cahokia Heights, IL
Elizabeth Mallott (Biology), Theresa Gildner (Anthropology), Claire Masteller (Earth, Environmental, and Planetary Sciences)

In the coming decades, localized flooding will become increasingly common in many communities due to climate change. This project will focus on Cahokia Heights, Illinois, using the town as a test case for how pervasive flooding in a lower-income community can negatively impact residents’ health. The team will study satellite-based data to compare Cahokia Heights’ flooding levels to those in surrounding towns. This data will be paired with soil and water samples to measure human pathogen exposure in the region. Critically, the team will also work to establish clear links between flooding and health outcomes.

Mobile Assessment of Daily Life Contexts Using Smartphones
Tammy English (Psychological & Brain Sciences), Jason Hassenstab (Neurology), Sojung Park (Brown School of Social Work), Ariela Schachter (Sociology)

Smartphone technology has the potential to revolutionize how scientists study people and intervene to change behavior. This project will put smartphones to use by measuring participants’ day-to-day activities. The team will recruit 50 younger and older adults who will install an app on their phones to gather a range of data about their daily environments. Combined with short surveys designed to supplement and verify the app’s measurements, the study will provide a richer picture of how people of different backgrounds navigate their everyday surroundings and interact with one another.

A Gaussian Process Framework for Idiographic Measurement of Psychological Traits
Roman Garnett (Computer Science & Engineering), Joshua Jackson (Psychological & Brain Sciences), Jacob Montgomery (Political Science)

Does each human being have a unique mental makeup, or do our psychology and personality traits share a common structure? To tackle this longstanding question, this project will apply machine learning to psychological assessments of 50 research subjects. The team will take more than 100 assessments of each subject across three weeks. The study of the resulting data could have intriguing implications for personalizing psychological diagnosis and treatment.

MOBOGEN: Data-Driven Modeling of Mobility, Borders, and Genetics
Michael Frachetti (Anthropology), Nathan Jacobs (Computer Science & Engineering), David Carter (Political Science)

Humans are an incredibly mobile species, having long since covered nearly every habitable region of the globe. This project will examine the “how” and the “why” of humanity’s movement across history, using global GPS data and real-time satellite imagery. Along the way, the MOBOGEN team will seek to answer core questions related to how geographic barriers and political borders impact human migration and ultimately our genetic heritage.

A Field Experimental Analysis of the Process and Implications of a Diverse and Inclusive Workplace: Social Identity, Communication, and Performance
Brent Hickman (Olin School of Business), Jessie Sun (Psychological & Brain Sciences)

Efforts to support diversity and inclusion in the workforce have attracted enormous attention from businesses, academics, and policymakers. But research into how diverse workplaces function is still lacking. This project will employ a field experiment approach – creating a real-world business environment that will help researchers understand how the diversity of work teams affects real-time interactions between team members.

The Relationship Between State Violence, Trust in Government, and Vaccine Uptake
Caitlin McMurtry (Brown School of Social Work), Michael Esposito (Sociology), Matthew Gabel (Political Science), Darrell Hudson (Brown School of Social Work)

The COVID-19 pandemic hit Black Americans especially hard, with notably higher rates of premature mortality than white Americans. But despite taking many other preventative measures to avoid COVID (such as masking and self-isolating), a disproportionately low number of Black Americans opted to receive the COVID-19 vaccine. This project proposes that this behavior could stem from a lack of trust in government, exacerbated by aggressive police practices in Black communities. To connect these two ideas, the group will compare ZIP code-level police data and vaccine adoption statistics, while also conducting focus group interviews to provide insight into the relationship between attitudes towards government, police practices, and the willingness of citizens to comply with public health recommendations.

Study of Polarization in Social Networks through Empirical Modeling and Simulations
William Yeoh (Computer Science & Engineering), Dino Christenson (Political Science)

Partisan polarization in the United States has risen dramatically in the past decade and is often linked to ideological “echo chambers” created by social media algorithms. However, these claims are often anecdotal and lack rigorous research. This team will construct an analytical framework to understand how ideology, emotions, and algorithms can impact political discourse. To achieve that goal, researchers will also analyze existing social media data to form a baseline for their simulations.

Conformal Prediction for Uncertainty Quantification in Emerging Application Areas, from Materials to Social Science
Robert Lunde (Statistics and Data Science), Robert Wexler (Chemistry), Betsy Sinclair (Political Science)

Conformal prediction is a powerful tool for predicting outcomes while making very few assumptions about real-world forces. This method for analyzing complex datasets is incredibly adaptable and especially helpful for characterizing uncertainty about potential outcomes. This team will apply and adapt conformal prediction for two sets of questions: Can we better predict how to combine chemicals to create breakthrough materials in renewable energy production? Can we use this methodology to better estimate public opinion for hard-to-reach populations?

Measuring Financial Innovation via Natural Language Processing
Ana Babus (Economics), Chenguang Wang (Computer Science & Engineering)

Over the past several decades, innovative investment methods — beyond traditional stocks and bonds — have changed how businesses raise money and opened new streams of potential revenue. These products are generally less transparent, less studied, and more loosely regulated. This has the potential to create market inefficiencies and, in extreme cases like the 2008 financial crisis, systemic risk resulting from poorly understood products spreading in concentrated sectors. This project will use natural language processing to study several decades of financial contracts, uncovering commonalities in these products and the firms that issue them. The goal is to better understand how market forces impact the evolution of these new investment methods.


For additional resources or support, please email Jacob Montgomery, Tammy English, or Bhavna Hirani.

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