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ARS SCINet and AI Center of Excellence Postdoctoral Fellows Program (FY2022)


In 2022, the SCINet Program in collaboration with the Artificial Intelligence Center of Excellence (AI-COE) provided funding to support 19 new postdoctoral fellows to be mentored by ARS scientists.

An 8-member panel composed of ARS scientists and National Program Leaders recently evaluated 64 proposals submitted by ARS scientist. Eight were selected for funding by SCINet, and 11 for funding by the AI-COE. These fellows will join our current group of fellows to result in nearly 30 fellows in our fellowship program.

The goal of the fellowship program is to develop the next generation of ARS scientists with expertise in conducting and leading individual and collaborative research using computationally-intensive approaches.

Each fellow is expected to participate in four types of activities: (1) individual computationally-intensive research with the mentor, (2) collaborative research with multiple ARS units, (3) co-leadership of a SCINet or AI COE working group, and (4) coordination of the Fellows Program. In addition, each fellow will have the opportunity to take advantage of training courses that build computational literacy, such as in data science, AI, bioinformatics, and geospatial analyses on our high performance computing clusters (Ceres, Atlas).

We congratulate the 19 ARS scientists who submitted successful proposals, and look forward to the exciting and impactful research to be conducted by the fellows, their mentors, and the collaborative working groups.

We expect to have another funding opportunity in FY23 so watch for that announcement in late 2022.

Proposals Funded in 2022 Mentor Co-mentor(s)
Developing tools for the real-time monitoring and query of all the world’s publicly available sequence data. Adam Rivers  
Determining the structure and function of proteins of foodborne and plant pathogens using Alphafold2 and top-down proteomic analysis Clifton K. Fagerquist  
AI-driven phenotype extraction from UAS imagery for crop genetics and breeding Jacob Washburn Alisa Coffin, Max Feldman
Machine learning approaches to gain structural and functional insights of genes regulating climate adaptability Carson Andorf Taner Sen
Inferring Protein Function for Enhanced Breeding using Machine Learning and Protein Structure Prediction Taner Sen Carson Andorf
A MATLAB toolbox for dynamic prediction of meteorological drought across Southern Great Plains Daniel Moriasi Ali Mirchi
Optimization of AI-based microscope image analysis with the Blackbird imaging robot Lance Cadle-Davidson Yu Jiang
Automated Detection of Insect Species in Food and Processing Environments using Artificial Intelligence Paul R Armstrong Lester Pordesimo
Improving the accuracy and scope of machine learning tools for camera trap-based ecological data analysis across US grazinglands Hailey Wilmer  
A broadly deployable deep learning workflow for image feature identification, segmentation, and data extraction. Devin A Rippner Jeff Neyhart , Kayla Altendorf, Garett Heineck, Andrew McElrone
An AI approach for discovering novel blast disease resistance sources in rice. Jeremy Edwards Yulin Jia
Predicting interspecies transmission of influenza A virus from swine to humans with machine learning Tavis Anderson Amy Vincent
Modeling the Spread and Adaptation of Stored-Product Insect Pests in the Face of Climate Change Alison Gerken Rob Morrison
Using AI to Analyze Climate Effects on Crop Performance Xianran Li  
AI/ML and Deep Learning to Enhance Understanding of Dietary Patterns that Promote Human Health David Baer Lauren O’Connor
The evolutionary genomics of Macrophomina phaseolina. Peter Montgomery Henry  
Spatial multi-criterion optimization of agricultural ecosystem services at the landscape scale Sarah Goslee  
Application of machine learning in livestock genomics George Liu  
Determining the pervasiveness of hybridization and introgression in agriculture and the driving mechanisms Christopher Owen  

Do you know someone who might be interested in becoming an ORISE Fellow with SCINet or the AI Center of Excellence? Contact the mentor listed above. To see all open opportunities, visit: