The ARS SCINet and AI Center of Excellence (AI-COE) funded 22 postdoctoral fellowship proposals in FY2023. The program was again very competitive, with many more proposals submitted than we could support. Information about the funded projects is provided below.
ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program - FY2023 Awards
| Title | Mentor | Co-mentor(s) |
|---|---|---|
| Determining the Evolutionary Potential of the Causal Agent of Coffee Leaf Rust Disease From Big Data | Rachel A. Koch Bach | |
| Solving Big Data Challenges in California’s Tree Nut Pests | Raman Bansal | |
| Developing A Cross-Kingdom Gene Editing Tool Kit For USDA-ARS Scientists | Paula Chen | Norman Best, David Kang, Bethany Redel, Adam Rivers, Jacob Washburn |
| AI Modeling of Grazingland Animal Behavior | Patrick Clark | |
| Networking local measurements to management scales using machine learning | Octavia Crompton | Kyle Knipper |
| Machine learning reveals RNAi molecular targets from transcriptomic architectures of mycotoxigenic fungi. | Milton Drott | Mitch Elmore, Hye-Seon Kim |
| Functional genomics and microbes for the Beenome100 pollinator project | Jay Evans | Judy Chen |
| Food Security Agency in Alaska | Claire Friedrichsen | Katie Pisarello, Dave Archer |
| Spatiotemporal effects of restoration on soil moisture dynamics in semiarid ecosystems | Alexander J. Hernandez | |
| Using AI to address large, complex datasets in microbiome-based IPM. | David Kang | |
| Using A.I. and Metatranscriptomics to Improve Predictions of Enzyme Function in the Gut Microbiome | Danielle G. Lemay | |
| Identification of fungal proteins and chemicals targeting insect pests | Brian Lovett | Kathryn Bushley |
| Transforming ARS scientists use of synthetic image creation pipelines for computer vision and AI | Steven Brian Mirsky | Chris Reberg-Horton |
| A supervised machine learning tool to predict parameters that are correlated with a reduction in antimicrobial resistance and probiotic administration | Adelumola Oladeinde | Zaid Abdo |
| Engineering agricultural products using an AI/ML approach to study insect enzyme and substrate interactions | Brenda Oppert | Gerard Lazo, Chris Mattison |
| Machine learning to distinguish pest from non-pest weevils | Lindsey Perkin | |
| Machine Learning Approaches to Improve Functional Predictions of Chemosensory Genes in Stored Product Insects | Erin Scully | |
| Implications of taxon-specific training sets on the accuracy of Google DeepVariant variant calling in non-model (non-human) systems for improved marker-assisted breeding and trait mapping. | Sheina Sim | Scott Geib |
| ASFV-Swine Reactome: Adapting Deep Learning to Predict Novel Protein Interactions | Edward Spinard | |
| Metagenomic analysis of winegrape soils to develop a unique approach to evaluate soil health outcomes | Kerri Steenwerth | Amisha Poret-Peterson, Cristina Lazcano, Jorge Rodgrigues |
| Robust explainable AI methods for understanding climate impacts on crop yields | Xuesong Zhang | Glenn E. Moglen, Kaiguang Zhao |
| Improve Aerial Image-based Forest Fire Detection Using Deep Learning | Huihui Zhang | David Barnard |