BSOS Faculty Receive 2026 Seed Grants from AIM

Three members of the College of Behavioral and Social Sciences faculty are helping lead projects that recently received funding from the Artificial Intelligence Interdisciplinary Institute at Maryland's (AIM) 2026 Research Seed Award Program.

In Maryland Today, Mel Coles shared summaries of each of their projects:

From Singular Trajectories to Shared Insights: Individualized and Generalizable AI Models of Social and Agency Development in Autism

Led by Clark Leadership Chair Professor Fengfeng Ke in the Department of Teaching and Learning, Policy and Leadership and Professor Yi Ting Huang of the Department of Hearing and Speech Sciences, the project will develop a dual-scale, competency-based modeling framework to trace the development of social-communicative and relational agency capacities in autistic adolescents while integrating practitioner expertise within computational modeling workflows. In partnership with three autism-serving organizations, the team will develop and refine analytic tools that combine insights from behavior analysts, therapists and educators with data-driven modeling.

From Bench to Bedside and Back Again: Co-Designing AI-Supported Clinical Interventions for Aphasia

Guided by clinical and human-computer interaction approaches, the project led by Assistant Professor Stephanie Valencia-Valencia of the College of Information and Professor Yasmeen Faroqi-Shah of the Department of Hearing and Speech Sciences will iteratively co-design AI-supported interventions for people with aphasia, as well as implement longitudinal personalization and evaluate the tools. Concurrently, it will uncover accessible interaction mechanisms, fine-tuned models and lucid explanations for AI support.

AFO Mapping and Spatially-Explicit Nutrient Transfer Optimization

Geographical sciences Assistant Professor Catherine Nakalembe and environmental science and technology Professor Stephanie Lansing are leading research to optimize the placement of waste-to-resource technologies across Maryland's agricultural landscape by resolving critical data gaps in animal feeding operation (AFO) mapping and crop nutrient demand. The project employs a geospatial AI approach that integrates deep learning with multisource satellite imagery for high-resolution AFO detection and nutrient supply and nutrient balance mapping, as well as geospatial optimization.