Description

This one-day, hands-on workshop introduces participants to single-cell RNA-seq analysis on RCAC systems using a 10x Genomics dataset and the Seurat framework. The workshop focuses on practical, end-to-end analysis, including quality control, normalization, clustering, visualization, and basic cell type annotation.

Emphasis is placed on running scRNA-seq workflows efficiently on RCAC HPC resources and understanding key analysis decisions commonly encountered in real-world single-cell studies.

Who should attend

• Researchers and students working with single-cell RNA-seq data
• Users planning to analyze 10x Genomics datasets
• Bioinformaticians seeking hands-on experience with Seurat on HPC systems

What you’ll learn

• scRNA-seq quality control and filtering using Seurat
• Clustering and visualization of single-cell data
• Downstream analysis and basic cell type annotation
• Practical considerations for running scRNA-seq workflows on RCAC

Level

Intermediate. Basic familiarity with RNA-seq concepts and the command line is recommended.

Registration: Register here (opens 04/09/2026)

Who Should Attend

Researchers interested in building reproducible, automated genomics workflows using modern workflow managers.

What You’ll Learn

• Core concepts behind reproducible workflows
• Structure and components of a Nextflow pipeline
• When and why to use workflow systems in bioinformatics

By the End of the Session, You’ll

• Understand how Nextflow enables reproducible analyses
• Be able to read and reason about simple Nextflow workflows
• Know how workflows fit into HPC and collaborative research

Level

Intermediate. Prior exposure to command-line tools is helpful.

🔗 Register now: Click here to register

Who Should Attend

Researchers working with sequencing data who want to understand and apply quality control best practices.

What You’ll Learn

• Common QC metrics for genomics data
• Tools used for assessing sequencing quality
• How QC decisions affect downstream analyses

By the End of the Session, You’ll

• Be able to interpret common QC reports
• Understand when data quality is sufficient for analysis
• Make informed decisions about filtering and preprocessing

Level

Basic. Suitable for newcomers to genomics data analysis.

🔗 Register now: Click here to register

Who Should Attend

Researchers and students who want to run bioinformatics software efficiently on RCAC clusters.

What You’ll Learn

• How software is deployed and accessed on RCAC systems
• Using modules, containers, and command-line tools
• Best practices for running analyses on shared HPC resources

By the End of the Session, You’ll

• Understand how to find and run bioinformatics software on RCAC
• Know when to use modules versus containers
• Avoid common execution and environment pitfalls

Level

Intermediate. Basic command-line familiarity recommended.

🔗 Register now: Click here to register

Description

This one-day, hands-on workshop introduces participants to genome annotation on RCAC HPC systems, covering both structural and functional annotation workflows. The workshop focuses on practical annotation strategies using modern tools such as BRAKER3, Helixer, Easel, and EnTAP, with an emphasis on reproducible execution on shared compute resources.

Participants will learn how to generate and evaluate gene models using transcriptomic and homology-based evidence, interpret annotation outputs, and assess annotation quality using standard metrics. The workshop emphasizes real-world considerations, common pitfalls, and troubleshooting strategies encountered in genome annotation projects.

Who should attend

• Researchers and students working with newly assembled genomes
• Bioinformaticians interested in gene prediction and annotation workflows
• Users planning to annotate genomes using RNA-seq or protein evidence
• Anyone seeking hands-on experience with annotation pipelines on RCAC

What you’ll learn

• Core concepts of structural and functional genome annotation
• How to run gene prediction workflows using BRAKER3 and Helixer on RCAC clusters
• Interpreting and refining gene model predictions
• Functional annotation using EnTAP
• Basics of annotation quality assessment and troubleshooting

Level

Intermediate. Participants should have basic familiarity with genomics concepts, command-line tools, and bioinformatics workflows.

Registration: Register here (opens 02/26/2026)

Who Should Attend

Researchers preparing figures for publications, presentations, or grant proposals using genomics data.

What You’ll Learn

• Principles of clear and effective data visualization
• Common pitfalls in genomics plots
• Tools and approaches for generating publication-quality figures

By the End of the Session, You’ll

• Be able to design clearer, more interpretable plots
• Understand how to tailor figures for different audiences
• Improve the visual quality of your genomics results

Level

Intermediate. Familiarity with basic plotting tools is helpful.

🔗 Register now: Click here to register

🔗 Register now: Click here to register

This workshop explores the evolving landscape of Geospatial Artificial Intelligence (GeoAI) on High-Performance Computing (HPC) environments.

We'll begin with the foundational concepts of GeoAI, introducing what typical GeoAI tasks are and how AI are applied to geospatial data like satellite imagery.

Next, we'll dive into the practical tools that enable this work, and set up working environment on HPC Clusters. We'll examine the TorchGeo library, which provides a comprehensive toolkit for training models on geospatial datasets within PyTorch. We'll also cover TerraTorch, a fine-tuning and benchmarking toolkit that extends capabilities for Geospatial Foundation Models.

Finally, we'll present several case studies to illustrate the transition from traditional, single-task GeoAI models—like a model trained to identify specific land cover types—to the latest advancements in Geoscience Foundation Models. These larger, more versatile models are capable of performing specific geoscience tasks with much less new data than would be required for a traditional model. We will also introduce three ways to perform these tasks with HPC, and mainly focus on two of them with online experiments: interactive job via OoD and batch job.

⏰ Time: 1:00 PM - 2:30 PM

💻 Location: Virtual (Teams)

🏫 Instructor: Jacob Verburgt

Who Should Attend

People who are starting to use High Performance Computing systems and want to learn how to use them. There is no prior knowledge/experience requirement to attend these workshops.

What You’ll Learn

By the end of the four 90 minute sessions, you’ll feel confident in your ability to navigate HPC systems and use them to run your workflow. You will learn basic Unix commands as well as HPC-specific ones. By the end, you should be equipped to create scripts and run applications on Purdue's supercomputing clusters.

Week 1 - Friday, March  6th, 2026 - Unix basics

Week 2 - Friday, March 13th, 2026 - HPC basics

Week 3 - Friday, March 20th, 2026 - Advanced Unix

Week 4 - Friday, March 27th, 2026 - Advanced HPC   Level

Beginner - No experience needed

🔗 Register now: LINK

The Rosen Center for Advanced Computing (RCAC) invites all students, faculty, staff, industry professionals, and beyond to register for this event, Women in HPC Presents: Intersectional Voices on Double Standards and Belonging, organized by the Purdue Women in High-Performance Computing (WHPC) chapter.

This will be a 90-minute workshop and panel exploring how double standards and bias shape belonging in computing and HPC. The event is co-designed with the Black Cultural Center (BCC), the Native American Educational and Cultural Center (NAECC), and the LGBTQ Center, centering intersectional perspectives across gender, race, indigeneity, and sexuality.

What to expect

Intersectional Panel (60 minutes): Panelists affiliated with BCC, NAECC, and the LGBTQ Center will share their lived experiences and observations of double standards in classrooms, labs, research groups, and campus life, followed by a guided discussion.

Audience: Students, postdocs, staff, and faculty in computing, data, and HPC, as well as campus community members interested in inclusive technical education.

We’re grateful to Aztlan Combat Sports for generously sponsoring this event.

Please complete the short registration form to help us plan. Feel free to share this invitation with your network; we look forward to seeing you there.

Description - Advanced RAG & Vector Databases As AI systems become more widely used in research and enterprise settings, one of the biggest challenges is helping large language models work effectively with an organization’s own data. Retrieval-Augmented Generation (RAG) provides a practical solution by combining LLMs with fast, accurate retrieval from internal knowledge sources. This is supported by vector databases, which store and search information in a way that aligns with modern embedding models. This session introduces the core concepts behind RAG workflows and explains how vector databases support reliable, relevant, and grounded AI outputs. We will discuss the practical design decisions, how to structure data, choose embeddings, build retrieval pipelines, and evaluate whether the system is working as expected.

Who Should Attend - AI/ML engineers, data scientists, research software engineers, platform engineers, and technical leads who want a practical, up-to-date understanding of how RAG systems work in real environments. This session is ideal for practitioners responsible for building LLM-powered applications, selecting retrieval or vector database tooling, or supporting researchers who need trustworthy, data-grounded AI systems.

Topics - What RAG does and why it matters for modern AI applicationsHow embeddings represent text and why vector databases are used to store themThe basic building blocks of a RAG pipeline: chunking, indexing, retrieval, reranking, and generationArchitectural patterns for building effective retrieval systemsWhat impacts the retrieval quality (better chunking, metadata, hybrid search)How to evaluate the RAG system

Level - Intermediate. Attendees should understand the basics of LLMs, but no prior experience with vector databases or RAG systems is required.

🔗 Register now: LINK

Who Should Attend

Life science researchers and students who want to use R more effectively for data manipulation, analysis, and visualization.

What You’ll Learn

• Core R data structures and workflows for biological data
• Importing, cleaning, and summarizing datasets
• Practical tips for writing clearer, reusable R code

By the End of the Session, You’ll

• Be comfortable working with common biological datasets in R
• Understand how to structure R scripts for analysis
• Have a foundation for further statistical or visualization work

Level

Basic. No prior R experience required.

🔗 Register now: Click here to register

Description

This one-day, hands-on workshop introduces participants to long-read genome assembly on RCAC HPC systems using modern assemblers such as HiFiasm and Flye. The workshop emphasizes practical considerations for running assembly workflows on shared compute resources, including parameter selection, workflow organization, and common troubleshooting scenarios.

Participants will also be introduced to hybrid assembly concepts, scaffolding approaches, and assembly quality assessment, with a focus on reproducible and scalable practices suitable for real-world genome assembly projects.

Who should attend

• Researchers and students working with long-read sequencing data
• Bioinformaticians interested in genome assembly on HPC systems
• Users planning to assemble genomes using PacBio HiFi or Oxford Nanopore data
• Anyone seeking hands-on experience with genome assembly workflows on RCAC

What you’ll learn

• How to run genome assembly workflows using HiFiasm and Flye on RCAC clusters
• Key considerations for long-read and hybrid genome assembly
• Basics of assembly evaluation, polishing, and visualization
• Practical strategies for organizing and troubleshooting assembly jobs on HPC systems

Level

Intermediate.

Registration: Register here (opens 02/05/2026)

This training focuses on building reliable and scalable LLM applications. The session covers how to enforce structured outputs (schemas, JSON, tool/function calling) to make model responses predictable and machine-readable, and how to design evaluation pipelines to systematically measure quality, detect regressions, and compare models. Participants will learn practical patterns for moving from ad-hoc prompting to robust, production-ready AI workflows.

🔗 Register now: LINK

Who Should Attend

Researchers and students who want to organize their bioinformatics projects to improve reproducibility, clarity, and long-term maintainability.

What You’ll Learn

• Recommended folder structures for genomics projects
• Naming conventions for scripts, data, and results
• Strategies for keeping analyses understandable months or years later

By the End of the Session, You’ll

• Have a practical project organization template
• Understand how good organization supports reproducibility
• Be able to restructure existing projects more effectively

Level

Basic. Suitable for users at all experience levels.

🔗 Register now: Click here to register

Who Should Attend

People who are starting to use High Performance Computing systems and want to learn how to use them. There is no prior knowledge/experience requirement to attend these workshops.

What You’ll Learn

By the end of the four 90 minute sessions, you’ll feel confident in your ability to navigate HPC systems and use them to run your workflow. You will learn basic Unix commands as well as HPC-specific ones. By the end, you should be equipped to create scripts and run applications on Purdue's supercomputing clusters.

Week 1 - Friday, February 6th 7th, 2026 - Unix basics

Week 2 - Friday, February 13th, 2026 - HPC basics

Week 3 - Friday, February 20th, 2026 - Advanced Unix

Week 4 - Friday, February 27th, 2026 - Advanced HPC

Level Beginner - No experience needed

🔗 Register now: LINK

Who Should Attend AI/ML engineers, data scientists, software developers, platform engineers, and advanced students who want to move beyond single-prompt LLM applications toward agent-based systems. Ideal for practitioners building multi-step, tool-using, or collaborative AI workflows that require structure, control, and reliability rather than ad-hoc prompting.

What You’ll Learn This session introduces agent-based LLM systems using LangGraph and AutoGen. Participants will learn core concepts behind agent workflows, how they differ from prompt-driven approaches, and how modern frameworks support structured, multi-step, and collaborative AI applications.

Level Intermediate. Prior experience with Python and basic LLM usage is recommended. 🔗 Register now:LINK

The AI landscape is evolving at an unprecedented pace, with rapid shifts in model architectures, tooling, deployment paradigms, and governance. This session provides a structured, practitioner-focused overview of the AI ecosystem as it stands in 2026, cutting through hype to focus on what is technically and strategically relevant. Rather than deep-diving into a single framework, the training maps the broader ecosystem—models, infrastructure, agent systems, multimodal AI, open vs. closed platforms, and emerging standards—to help participants make informed design and investment decisions.

Who Should Attend

AI/ML engineers, data scientists, research software engineers, platform engineers, technical leads, and advanced students who want a clear, up-to-date understanding of the modern AI stack. This session is ideal for practitioners responsible for architectural decisions, tooling selection, or long-term AI strategy, as well as researchers transitioning from experimental LLM usage to production-grade systems.

What You’ll Learn

Participants will gain a high-level but technically grounded understanding of the current AI ecosystem, including:

How the AI stack has evolved from single-model usage to full AI systems (models, agents, tools, orchestration, and infrastructure)

Key trends shaping 2026, including agentic workflows, multimodal models, open-source vs. commercial trade-offs, and on-prem vs. cloud deployment

The role of orchestration frameworks, vector databases, evaluation tooling, and governance layers

Practical guidance on which developments matter for real-world research, enterprise, and academic use cases—and which are largely noise

How to reason about future-proofing AI systems amid rapid ecosystem churn

Level

Intermediate. Familiarity with basic machine learning concepts and prior exposure to large language models (e.g., APIs, notebooks, or simple applications) is recommended, but deep expertise in any single framework is not required. 🔗 Register now: LINK

Who Should Attend

Researchers, graduate students, and postdocs who want to perform RNA sequencing (RNA-seq) analysis on Purdue’s high-performance computing (HPC) systems. Participants should have basic command-line familiarity but no prior experience with RNA-seq analysis is required. You do not need an existing RCAC account; workshop access will be arranged for attendees.

What You’ll Learn

This hands-on workshop introduces participants to the complete RNA-seq analysis workflow, from accessing RCAC systems to generating differential expression results. Emphasis will be on practical execution and reproducibility, with minimal theoretical background.

By the End of the Session, You’ll

• Access and manage your environment on RCAC clusters.
• Perform pre-alignment quality control and reference indexing.
• Align RNA-seq reads, evaluate mapping quality, and visualize results.
• Quantify transcript abundance and identify differentially expressed genes.
• Use Kallisto for lightweight, reference-free quantification.

Level

Intermediate, suitable for those comfortable with the Linux command line who wish to gain practical experience in running RNA-seq analyses on an HPC cluster.

Registration: Register for Event (opens 01/08/2026)

Who Should Attend

Researchers and students who want to develop a clearer mental model for bioinformatics problem-solving, tool selection, and analytical decision-making.

What You’ll Learn

• How bioinformaticians break down biological questions into computational tasks
• Common analysis patterns across genomics workflows
• How to reason about data types, assumptions, and limitations

By the End of the Session, You’ll

• Be able to frame biological questions in computational terms
• Recognize common workflow components across genomics analyses
• Make more informed decisions when choosing tools and approaches

Level

Basic. No prior bioinformatics experience required.

🔗 Register now: Click here to register

This is a 4-week workshop that will happen on Fridays in January from 1:00 PM - 2:30 PM Eastern time.

Who Should Attend People who are starting to use High Performance Computing systems and want to learn how to use them. There is no prior knowledge/experience requirement to attend these workshops.

What You’ll Learn

By the end of the four 90 minute sessions, you’ll feel confident in your ability to navigate HPC systems and use them to run your workflow. You will learn basic Unix commands as well as HPC-specific ones. By the end, you should be equipped to create scripts and run applications on Purdue's supercomputing clusters.

Week 1 - Friday, January 9th 7th, 2026 - Unix basics

Week 2 - Friday, January 16th, 2026 - HPC basics

Week 3 - Friday, January 23rd, 2026 - Advanced Unix

Week 4 - Friday, January 30th, 2026 - Advanced HPC

Level Beginner

🔗 Register now: LINK