Announcing the 2026-2027 Tulane University Data, AI, or Community-Engaged Research/Project Grant Awardees
Tulane University’s Connolly Alexander Institute for Data Science (CAIDS), Center for Community-Engaged Artificial Intelligence (CEAI), and Center for Public Service (CPS) collectively offered grant funding for up to $10,000 in Spring 2026. These grants support research projects involving data science, artificial intelligence (or its impacts), and/or community-engaged research or projects. Projects that met multiple criteria were prioritized. Full-time faculty, post-doctoral scholars, and advanced graduate students were eligible for grant funding.
Meet the Awardees
Tori Bush, Postdoctoral Fellow, Tulane Global Humanities Center
Project: The New Orleans Water Democracy Community Archive (NOWDCA)
Dr. Bush’s project, The New Orleans Water Democracy Community Archive (NOWDA), will create a publicly accessible digital archive that documents community work advancing water democracy goals by preserving and amplifying long‑standing knowledge, strategies, and lived experiences of resisting inequitable water infrastructure. For more than 35 years, residents of the Lower and Upper Ninth Ward have organized against the Army Corps of Engineers’ Industrial Canal project, a federally driven plan that threatens public health, cultural continuity, and environmental future. This long arc of resistance which has been sustained through neighborhood meetings, political advocacy, legal challenges, and storytelling, has generated a rich but dispersed body of community knowledge that remains at risk of being lost as elders age, materials deteriorate, and records remain scattered across personal collections. NOWDA responds to this urgent gap by creating a publicly accessible digital archive that preserves oral histories, documents, media, and organizing materials, ensuring that the strategies, memories, and lived expertise of Ninth Ward residents remain central to ongoing struggles for water democracy and environmental justice.
Arachu Castro, Samuel Z. Stone Endowed Chair of Public Health in Latin America, Director of the Center for Health Equity in Latin America, Celia Scott Weatherhead School of Public Health and Tropical Medicine
Project: Machine Learning Models for Early Detection of Violent Discipline and Cognitive Stimulation Deprivation among Dominican Children
Dr. Arachu Castro’s project, Project SIRENI, will develop and validate interpretable machine learning models that predict violent discipline and cognitive stimulation deprivation in children under 5 in the Dominican Republic. To do so, Dr. Castro and IHSD doctoral student Nora Badoui-Rodríguez will leverage an existing, de-identified dataset of 1,019 mothers collected from 23 Instituto Nacional de Atención Integral a la Primera Infancia (INAIPI) centers in Santo Domingo, generate actionable, culturally grounded risk profiles for INAIPI frontline workers, and co-develop a risk assessment protocol, implementation guide, and training materials with INAIPI.
Alexis Culotta, Senior Professor of Practice, School of Liberal Arts
Project: Artistic Network Toolkit
Dr. Culotta’s grant will support the next phase of growth for the Artistic Network Toolkit (ANT), an open-source platform that allows scholars to transform art historical datasets into dynamic network visualizations without specialized computational training. In this next phase, ANT will expand its capacities from the perspective of museum collections, working with local museums like the Ogden Museum of Art to explore how the tool's expansion, along with the potential integration of a human-AI pipeline, might benefit the tool's overall capacities and integrity for research.
Simone Domingue, Research Assistant Professor, ByWater Institute
Co-Investigators:
Dr. Aishwarya Borate, Research Scientist, ByWater Institute
Dr. Ibrahim Demir, Presidential Chair in Informatics and Artificial Intelligence, ByWater Institute
Dr. Yusuf Sermet, Research Professor, ByWater Institute
Project: Co-Designing an AI-Assisted Climate Resilience Planning Tool
Dr. Domingue’s project will facilitate climate-hazard resilience planning by co-producing an AI-assisted tool through existing community relationships with Tulane University ByWater Institute’s Southwest Louisiana and Central Acadiana Resilient Future (SWLCA-RF) program. The SWLCA-RF works across 20 parishes in Louisiana to build regional capacity for climate resilience and adaptation. The overarching question this project will answer is: how can co-created AI-assisted tools be leveraged for collaborative hazard resilience planning? Tulane University will leverage a database they have already developed, and their connections with community partners and collaborators, to co-produce a tool that allows for systematic comparison of existing and/or potential resilience projects, programs, and actions across multiple governance levels and jurisdictions (e.g., town, city, parish, Tribe, etc.). The tool will draw from an existing database of different types of local and regional government plans, and technical documents, to identify assets within a watershed, or specific communities, most vulnerable to climate impacts, and where hazard mitigation actions are lacking. The tool will also assist planners and community members with the prioritization or evaluation of regional projects.
Cynthia Ebinger, Jefferson Science Fellow, Bureau of Oceans, International Environment, and Science, US Dept. State, Marshall-Heape Chair in Geology, School of Science and Engineering
Project: AI-driven detection and characterization of earthquakes for real time volcanic eruption warning in Rwanda and DRC
Dr. Cynthia Ebinger’s project focuses on AI-driven detection and characterization of earthquakes for real time volcanic eruption warning in Rwanda and the Democratic Republic of the Congo (DRC). Building on 16 years of work with scientists and policy makers in Rwanda and Democratic Republic of the Congo to improve volcano early warning systems for Nyiragongo and Nyamuragira, she and graduate student Cirus Kalugana will train, benchmark, and implement ML-based and AI tools for low frequency event detection, improve event separation in dense swarms, apply the workflows to data from 2012-2024, and share detection and location algorithms with community partners in Rwanda and DRC.
Saad Hassan, Assistant Professor, School of Science and Engineering
Project: Making Personal Health Visualizations Accessible for People with Disabilities
Dr. Saad Hassan’s project focuses on AI-enabled multimodal interfaces for blind and low vision (BLV) users in exploring local patterns and relationships in personal health visualizations. Using a research-through-design approach that centers participatory design with BLV users, the project will introduce open-source visualization accessibility techniques, compare and map these techniques to specific health app contexts, co-ideate natural language–based interfaces grounded in the latest advancements in AI, and develop a prototype application. The project will produce co-designed prototypes, design guidelines, and empirical evidence demonstrating new paradigms in accessible health visualization.
Patricia Kissinger, Professor, Associate Dean for Faculty Affairs and Development, Celia Scott Weatherhead School of Public Health and Tropical Medicine; Hua He, Professor, Celia Scott Weatherhead School of Public Health and Tropical Medicine
Project: Evaluating Machine Learning Predictive Models for Chlamydia trachomatis infection in a community screening cohort of young African Americans in New Orleans
This project evaluates whether machine learning models can improve prediction of chlamydia positivity in a community-based STI screening cohort. Co-PIs Drs. Patricia J. Kissinger and Hua He will supervise and mentor doctoral staff Akilesh Kandregula. Kandregula will serve as the lead programmer. Using data from the Check It I Study, You Geaux Girl, and Check It II, the project will compare unpenalized logistic regression, ridge, lasso and elastic net logistic regression, random forest, and gradient boosting models, evaluate predictive performance, calibration, interpretability, generalizability, and practical usefulness, and develop preliminary data, design approach, and implementation rationale for an NIH grant application focused on an app-based chlamydia screening support tool. Building on a long-standing foothold in the community with a network of partners that include barbers, historically Black Colleges and Universities, community colleges, recreational facilities, small Black owned businesses, churches, and many community-based organizations, the project uses community-based STI screening programs designed to reach Black youth in New Orleans through venue-based recruitment in non-clinical community settings.
Fariba Mamaghani, Assistant Professor, A. B. Freeman School of Business
Project: Contract Choice and Commitment Concessions: Evidence from Individual-Level Electricity Consumption and Payments
Dr. Fariba F. Mamaghani's project estimates the causal effect of electricity contract choice on the rates households actually pay, a decision growing more consequential as electricity bills rise faster than inflation. In deregulated retail markets, consumers choose between fixed rate contracts, which lock in a price in exchange for a term commitment, and variable rate contracts, whose prices move month to month, yet the true cost of either choice is hidden behind non-linear pricing, price volatility, and consumer self-selection. This is the first empirical study to examine this choice using individual-level consumption and payment data from a deregulated market, drawing on a proprietary dataset of nearly 80,000 residential households observed hour by hour over a full year, which Dr. Mamaghani enriches with Google search intensity data and Census demographic data to build one of the richest datasets in the field. At its core, the project asks a question every household in a deregulated market face: is committing to a fixed rate truly cheaper, and if so, for whom? Combining a Two-Stage Least Squares strategy with Double Machine Learning, this ongoing research suggests that utilities reward commitment with a genuine rate concession, but that a household's load shape decides who earns it. The findings point to concrete design levers for utilities, from enhanced facts labels that quote prices by consumption variability, to personalized price quotes computed from each customer's own consumption history, to contracts shaped around the winter seasons where households spend the most (interestingly not summer), while consumers learn to judge contracts through their own consumption patterns rather than the few representative prices on a facts label. Dr. Mamaghani is collaborating with one of the largest utility companies in the United States to put these insights into practice. This work was recently presented at Harvard Business School.
Vanessa W. Menard, PhD candidate, School of Medicine
Project: Benchmarking and Standardizing Interpretation of the Single Antigen Bead HLA Antibody Assay
Vanessa’s project focuses on benchmarking and automating interpretation of the single antigen bead (SAB) HLA antibody assay used in transplantation. Through reciprocal collaborations with UPenn, UCLA, SUNY Upstate, and Duke, the project will build the field's first benchmark for antibody analysis using SAB assay data and relevant HLA typing from antibody characterization and virtual crossmatch proficiency testing materials from American Society for Histocompatibility & Immunogenetics (ASHI) and College of American Pathologists (CAP), with integration of the UCLA Serum Exchange and crowdsourcing for additional benchmark antibody cases.
She is formalizing the multidimensional reasoning used by histocompatibility specialists into a deterministic, rule-based computational method for automated SAB data interpretation, validated by the benchmark. Each positive antibody call records the rules that fired and the supporting evidence, so reasoning is human-readable and results are auditable. The project's AI component will use HEROS, the Heuristic Evolutionary Rule Optimization System, to optimize rules and weights through machine learning while preserving interpretability.
The method will then be applied to multicenter pre- and post-transplant SAB data from approximately 10,000 kidney transplants to study antibody reactivity and its association with graft outcomes linked through the Scientific Registry of Transplant Recipients (SRTR).
Jylana L. Sheats, Ph.D., Clinical Associate Professor, Celia Scott Weatherhead School of Public Health and Tropical Medicine
Project: The WEATHER (Weather-related Exposure and the Health, Emotional, and Response Outcomes) Study: A Community-Engaged Pilot Study in North Nashville, Tennessee
Dr. Sheat’s community-engaged mixed-methods research project addresses the need for research on the long-term and downstream health effects of extreme weather and weather-related natural disasters, particularly among populations disproportionately affected by structural inequities. This research project will be in collaboration with Fisk University’s John Lewis Center for Social Justice (academic and community organization partner), the Climate Mental Health Network, and the EcoHealing Project (community organization), generating locally grounded data to inform preparedness, recovery, and intervention strategies aimed at reducing the health impacts of extreme weather and weather-related natural disasters in communities at heightened risk. How does cumulative exposure to extreme weather and weather-related natural disasters shape emotional well-being and behavioral and social responses among Black adults in North Nashville, and how do researcher-led, AI-assisted, and AI-generated qualitative analytic approaches influence the interpretation of these lived experiences? Thus, the aims are to: (1): Characterize cumulative exposure to extreme weather and weather-related natural disasters and related disruptions to social determinants of health among Black adults in North Nashville; (2): Examine associations between exposure, emotional well-being, and behavioral and social responses; and (3): Compare researcher-led (manual), AI-assisted, and AI-generated qualitative analyses to assess differences in interpretation of lived experience data. A portion of this work is also supported by the Tulane University Carol Lavin Bernick Faculty Grant Program.
Heather Veneziano, Program Director for Historic Preservation, Professor of Practice in Historic Preservation, School of Architecture
Project: Predictive Landscape Modeling for Identifying Unmarked Cemeteries of the Enslaved in Louisiana’s River Parishes
Heather Veneziano’s project evaluates whether consistent and meaningful spatial patterns associated with known plantation cemeteries can be identified within a defined landscape and modeled to generate probabilistic maps highlighting areas of higher likelihood for undocumented burial sites in St. James Parish and St. John the Baptist Parish. With support from the Louisiana Bucket Brigade and the Environmental Integrity Project, and through the integration of geospatial analysis, archival research, machine learning methods, spatial data, historical records, and community knowledge, the project will develop interpretable probability zones, support the recognition and protection of burial landscapes that have historically been erased or overlooked, and establish a scalable, community-engaged framework for identifying and protecting vulnerable cultural landscapes.