- Registration: Register Here
Forum Goal: To advance the appropriate and impactful use of AI tools, techniques, and infrastructure across the ARS, REE, and USDA research portfolio.
Objectives
- Demonstrate Relevance – Exposing researchers at all skill levels to the potential of AI tools through compelling case studies and examples.
- What is AI and how/why should I use it as a federal employee?
- How can AI streamline my research and increase my impact?
- Build Connections – Helping users connect, network with and learn from their colleagues and collaborators.
- Who are the current users of AI tools in my agency?
- Inspire Agility – Sparking the imagination of researchers and technicians driving new research impact through adoption and appropriate use of AI tools in research.
- Using new tools, what new research questions could I ask and how can I have more impact?
- Catalyze Innovation– Fostering collaborative innovation by making practical, hands-on training opportunities to ensure appropriate and impactful use.
- What is it that I need to know and where do I start?
- What enablers does REE have access to and what’s missing?
Workshops
Training workshops are planned for the final two days of the forum. You will be able to choose from the following when submitting your online registration. Note that space is limited, so each workshop has a registration cap.
About Workshop Prerequisites
The technical workshops each have one or more required prerequisite skills. If you do not yet have the prerequisite skills for a workshop, please do not let that deter you from signing up! We will offer opportunities for workshop registrants to learn all required prerequisite skills. In some cases, this will be through other workshops offered at the Forum. In other cases, this will be through virtual training options that we will offer in the weeks leading up to the Forum. We will contact workshop registrants about these pre-Forum training opportunities.
November 20, Afternoon — Foundational Skills and Concepts
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An introduction to machine learning for science
Machine learning underlies the vast majority of modern AI methods, including the ever-expanding applications of deep learning and generative AI. This workshop will give participants a hands-on introduction to the basic concepts and techniques needed to understand machine learning and to apply machine learning methods to scientific research.
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Data preparation and quality assessment in genome assembly and annotation
In this workshop, participants will explore techniques for evaluating the accuracy and completeness of genome assemblies and annotations, helping attendees understand key metrics and statistical methods used to assess the quality of genomic data.
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AI project and product management
Effective project management is crucial for the successful implementation of AI initiatives. This workshop provides a framework for managing AI projects from inception to completion, integrating project management methodologies with the unique challenges and opportunities presented by AI projects.
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November 21, Morning — Applications of AI
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Predicting functional roles of proteins using AI-driven bioinformatics tools
In this hands-on workshop, participants will learn how to predict the functional roles of proteins by analyzing their sequence data using state-of-the-art bioinformatics tools powered by AI. The focus will be on understanding how AI-based methods are applied to predict protein characteristics and other downstream uses for gene annotations.
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Computer vision I: introduction and image classification
This workshop will teach participants the concepts and tools they need to begin applying modern, deep learning-based computer vision methods to their own scientific research. This will be an interactive, hands-on workshop that will offer plenty of opportunities for practice and experiential learning. By the end of the session, participants will have trained and evaluated a state-of-the-art image classification model on a custom image dataset.
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Data management planning for AI
This workshop will help participants learn how to address AI-related research data (e.g., training datasets) in Data Management Plans (DMPs). There will be a brief presentation on DMPs by the National Agricultural Library (NAL) followed by examples of DMPs for research involving AI and discussions on common challenges and solutions for developing DMPs.
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From reads to variants: a pipeline for variant calling using DeepVariant
DeepVariant is a DNA sequence variant caller that uses a convolutional neural network (CNN) to call genotypes relative to a reference genome assembly. In this workshop, we will discuss a workflow for calling variants from whole-genome data for multiple individuals.
November 21, Afternoon — Applications of AI
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Computer vision II: object detection and semantic segmentation
In this workshop, participants will learn the key concepts and techniques needed to use modern, deep learning-based computer vision methods for object detection and semantic segmentation. Learners will practice training and evaluating state-of-the-art computer vision models on custom image datasets.
This workshop is intended as a continuation of “Computer vision I: introduction and image classification”, but participants do not need to take the earlier workshop if they already have a basic knowledge of machine learning and computer vision concepts.
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Protein structure prediction, search, and analysis with AI
In this workshop, participants will learn how to use cutting-edge, AI-based tools for analyzing protein structure and function.
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Spatial modeling with machine learning
This workshop will explore examples of spatial modeling tasks (e.g., spatial interpolation from point data to gridded data) with machine learning methods. The content of the session will primarily focus on the spatial component (e.g., how to include spatial proximity as a predictor) although machine learning concepts will be discussed as relevant.