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 Title | Institution | PI | Award Year |
|---|---|---|---|
| LP: MoDaCoM-TL: Model and Data Compatibility Metric for Transfer Learning | A-State | Jason Causey | 2022 |
| DC/LP: Smart curation and deep learning-based enhancement of social risk data | UAMS, A-State | Melody Greer | 2022 |
| CI: AI-Supported Cyberinfrastructure for Scalable Flood Resilience Assessment | UAF | Xiao Huang | 2022 |
| LP: Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson’s Disease | UAMS | Yasir Rahmatallah | 2022 |
| DC/LP: Developing Machine Learning Models to Improve the Effectiveness of Automated Data Curation Processes | UALR | Ahmed Abu Halimeh | 2022 |
| ED: Development of Interdisciplinary Research Collaborative to Provide Datasets in Support of Education Research in Data Science | ATU | Weijia Jia | 2021 |
| LP: Toward fair and reliable consumer acceptability prediction from food appearance | UAF | Dongyi Wang, Han-Seok Seo, and Shengfan Zhang | 2021 |
| ED: Geospatial Data Science in Public Health: Inter-institutional educational collaboration to enhance data science curriculum in Arkansas | UAMS | Sean Young | 2021 |
| LP: Interpretable Multimodal Fusion Networks for Fault Detection and Diagnostics of Two-Phase Cooling Under Transient Heat Loads | UAF | Han Hu | 2021 |
| ED: Piloting Big Data Science in Arkansas Middle School Classrooms | UAMS | Kevin Phelan and Tiffany Huitt | 2021 |
| LP: Machine Learning-based emulation and prediction in ensembles in disordered photocatalytic composites | UAF | Rob Coridan | 2021 |
| LP: AgAdapt: An evolutionarily-informed algorithm for genomic prediction of crop performance in novel environments | A-State | Emily Bellis | 2021 |
| LP: Machine Learning for Predicting Refugee Counts | UALR | Esther Ledelle Mead | 2021 |
| ED: Crying Out Data Science in the Center of Arkansas: Invitation for High School Students to the World of Data Science | UCA | Yeil Kwon and Nesrin Sahin | 2021 |
| LP: Generating Big Radiogenomic Data of Cancer Using Deepfake Learning Approach | PSC | Suzan Anwar | 2021 |