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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 (e.g., Python, Jupyter notebooks, command line basics). 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 (e.g., for basic machine learning knowledge). In other cases, this will be through virtual training options that we will offer in the weeks leading up to the Forum (e.g., for basic Python skills). We will contact workshop registrants about these pre-Forum training opportunities.
November 20, Afternoon — Foundational Skills and Concepts Workshops
An introduction to machine learning for science
1:00 PM – 4:00 PM | cap at 45
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. Participants will learn how to train, evaluate, and use a variety of machine learning models for data analysis tasks. This session will also help participants critically evaluate the use and application of machine learning in science.
(Lead(s): ARS SCINet Office)
Prerequisites:
- Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.
Data preparation and quality assessment in genome assembly and annotation
1:00 PM – 4:00 PM | cap at 35
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. Knowing how to evaluate a genome will ensure reliable results for downstream, AI-based analyses like gene model prediction, variant detection, or comparative studies. Participants will also learn how to extract the transcripts and proteins from their genomes, to enable a variety of downstream AI-based applications, such as protein structure prediction. By the end of the workshop, attendees will be better equipped with the practical skills necessary to evaluate genomes and annotations for a range of bioinformatics applications.
(Lead(s): Genome Informatics Facility at Iowa State University)
Prerequisites:
- Familiarity with basic command-line concepts. We will offer virtual training for these skills before the Forum begins.
AI project and product management
1:00 PM – 4:00 PM | cap at 55
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. Attendees will explore the CRISP-DM framework, the OSEMN framework, and other key challenges unique to AI projects such as defining a performance-based project scope, building a successful team, and model support considerations for long term success. This workshop is ideal for AI project managers and business leaders looking to guide technical resources toward successful implementation of AI.
(Lead(s): Nick Pallotta PMP, NASS, Head of Workforce Performance and Staff Development)
Prerequisites:
- None
November 21, Morning — Applications of AI Workshops
Computer vision I: introduction and image classification
9:00 AM – 12:00 PM | cap at 35
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.
(Lead(s): ARS SCINet Office)
Prerequisites:
- Familiarity with basic machine learning concepts. The workshop on November 20 will provide this background, if needed.
- Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.
Predicting functional roles of proteins using AI-driven bioinformatics tools
9:00 AM – 12:00 PM, 1:30 PM – 4:30 PM (All-Day Course) | cap at 35
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. Two such examples will be predicting signal peptides (indicators of protein secretion) and subcellular localization (where the protein operates in the cell). Participants will use sample datasets to explore how computational models can interpret protein sequences and provide insights into their biological roles. By the end of the session, attendees will have the knowledge and skills to functionally annotate proteins in any gene annotation.
(Lead(s): Genome Informatics Facility at Iowa State University)
Prerequisites:
- Familiarity with basic command-line concepts. We will offer virtual training for these skills before the Forum begins.
Data management planning for AI
9:00 AM – 11:00 AM | cap at 55
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.
(Lead(s): NAL)
Prerequisites:
- None
From reads to variants: a pipeline for variant calling using DeepVariant
9:00 AM – 12:00 PM | cap at 45
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. This workflow involves trimming and filtering raw reads, mapping them to a reference assembly, calling variants for each individual, merging the variants of all individuals into a single variant call format file (.vcf), and filtering the resulting variant file. We will guide participants through this pipeline step by step, providing generalized commands for each phase of the process, as well as strategies for optimizing cluster usage and reducing compute time. The final product will be a .vcf containing variants for all individuals which can be used for downstream analyses, along with a solid understanding for performing variant detection using DeepVariant.
(Lead(s): ARS scientists Sheina Sim and Craig Carlson)
Prerequisites:
- Familiarity with basic command-line concepts. We will offer virtual training for these skills before the Forum begins.
- Understanding of genomic sequencing.
- General optimism.
November 21, Afternoon — Applications of AI Workshops
Computer vision II: object detection and semantic segmentation
1:30 PM – 4:30 PM | cap at 35
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.
(Lead(s): ARS SCINet Office)
Prerequisites:
- Familiarity with basic machine learning concepts. The workshop on November 20 will provide this background, if needed.
- Familiarity with basic computer vision concepts (e.g., an understanding of how image data are structured in computer memory). The morning computer vision workshop will provide this background.
- Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.
Protein structure prediction, search, and analysis with AI
1:30 PM – 4:30 PM | cap at 35
In this workshop, participants will learn how to use cutting-edge, AI-based tools for analyzing protein structure and function. The workshop will start by exploring 3D protein structure prediction using AlphaFold for alignment-based structure prediction and ESMFold for single-sequence structure prediction. Participants will then learn how to use FoldSeek for structure-based protein similarity search. The last part of the workshop will bring all of these concepts together by using PanEffect to explore how genetic variations in protein sequence can influence an organism’s phenotype.
(Lead(s): ARS scientists Hye-Seon Kim and Carson Andorf)
Prerequisites:
- Familiarity with basic command-line concepts. We will offer virtual training for these skills before the Forum begins.
Spatial modeling with machine learning
1:30 PM – 4:30 PM | cap at 45
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. The goals of this session are to 1) introduce key concepts about incorporating spatial data in machine learning and 2) provide examples in Python on how to manipulate spatial datasets to use in machine learning functions, compare the performance of machine learning approaches for spatial prediction, and visualize observed spatial data and the prediction results.
(Lead(s): ARS SCINet Office)
Prerequisites:
- Familiarity with basic machine learning concepts. The workshop on November 20 will provide this background, if needed.
- Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.