The DART Research Seed Grant Program invites scientists throughout Arkansas to identify emerging or transformative areas of research in alignment with DART scientific focus and support one or more of the DART goals, but does not overlap current DART projects. This identified research should strengthen ties to Arkansas business; enhance our talent pool by expanding and/or leveraging research collaborations; or provide unique educational or training opportunities. Read more about the Research Seed Grant RFP.

Proposal TitleInstitutionPIAward Year
LP: MoDaCoM-TL: Model and Data Compatibility Metric for Transfer LearningA-StateJason Causey2022
DC/LP: Smart curation and deep learning-based enhancement of social risk dataUAMS, A-StateMelody Greer2022
CI: AI-Supported Cyberinfrastructure for Scalable Flood Resilience AssessmentUAFXiao Huang2022
LP: Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson’s DiseaseUAMSYasir Rahmatallah2022
DC/LP: Developing Machine Learning Models to Improve the Effectiveness of Automated Data Curation ProcessesUALRAhmed Abu Halimeh2022
ED: Development of Interdisciplinary Research Collaborative to Provide Datasets
in Support of Education Research in Data Science
ATUWeijia Jia2021
LP: Toward fair and reliable consumer acceptability prediction from food appearanceUAFDongyi Wang, Han-Seok Seo, and Shengfan Zhang2021
ED: Geospatial Data Science in Public Health: Inter-institutional educational
collaboration to enhance data science curriculum in Arkansas
UAMSSean Young2021
LP: Interpretable Multimodal Fusion Networks for Fault Detection and Diagnostics of Two-Phase Cooling Under Transient Heat Loads UAFHan Hu2021
ED: Piloting Big Data Science in Arkansas Middle School ClassroomsUAMSKevin Phelan and Tiffany Huitt2021
LP: Machine Learning-based emulation and prediction in ensembles in disordered photocatalytic compositesUAFRob Coridan2021
LP: AgAdapt: An evolutionarily-informed algorithm for genomic prediction of crop performance in novel environmentsA-StateEmily Bellis2021
LP: Machine Learning for Predicting Refugee CountsUALREsther Ledelle Mead2021
ED: Crying Out Data Science in the Center of Arkansas: Invitation for High School Students to the World of Data ScienceUCAYeil Kwon and Nesrin Sahin2021
LP: Generating Big Radiogenomic Data of Cancer Using Deepfake Learning ApproachPSCSuzan Anwar2021