Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Biologists curious about using AI assistants and large language models in everyday research work: writing and debugging code, reading documentation, and drafting analyses.

What You’ll Learn

• Where AI tools genuinely help in a bioinformatics workflow, and where they mislead
• How to use an AI assistant to write, explain, and debug analysis code
• How to check AI-generated output against your data, and what not to share with a model

By the End of the Session, You’ll

• Use an AI assistant to draft and debug a script for a real task
• Evaluate an AI answer critically before acting on it
• Decide which parts of your work are appropriate for AI assistance

Level

Basic. No programming experience required.

Register now: Click here to register

Artificial intelligence is becoming increasingly embedded in how researchers conduct scientific work and how educators design, deliver, and support learning. Researchers are using AI to explore literature, write and debug code, analyze data, generate hypotheses, automate repetitive workflows, and communicate scientific results. At the same time, educators are experimenting with AI-assisted tutoring, assessment, course development, feedback, and personalized learning experiences.

This session explores how AI can be used practically across scientific research and education while also examining the limitations and risks that come with these tools. We will discuss where AI can genuinely improve productivity and discovery, where human expertise remains essential, and how issues such as reliability, reproducibility, privacy, and responsible use should be considered. Practical examples will highlight how researchers, faculty, and technical teams can incorporate AI into existing workflows without treating it as a replacement for scientific judgment or instructional expertise.

Who Should Attend Faculty, researchers, graduate students, research software engineers, educators, instructional staff, data scientists, and technical professionals interested in applying AI within research or teaching environments. The session is especially relevant for those exploring how generative AI and emerging AI tools can support scientific and educational workflows.

Topics

AI-assisted scientific discovery and research workflows Literature review, synthesis, and knowledge exploration AI for coding, debugging, and scientific software development Data analysis, modeling, and scientific computing Generative AI for teaching, tutoring, and learning support AI-assisted course and educational content development Reliability, reproducibility, privacy, and responsible use Practical applications across scientific and academic disciplines

Level Beginner to Intermediate. No specialized AI background is required, although familiarity with common AI or generative AI tools will be helpful.

🔗 Register now: LINK

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers who analyze biological data in R and want to run RStudio and Bioconductor packages on RCAC clusters instead of an overloaded laptop.

What You’ll Learn

• How to start RStudio on RCAC clusters and request the right amount of memory and time
• What the r-bioconductor environment provides and how to add packages of your own
• How to move an analysis from an interactive RStudio session to a batch job when it outgrows the session

By the End of the Session, You’ll

• Run RStudio on an RCAC cluster with resources sized to your data
• Load Bioconductor packages from the provided environment and install additional ones
• Submit an R script as a batch job for longer analyses

Level

Basic. Basic familiarity with R is helpful.

Register now: Click here to register

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLMs in external, up-to-date, or proprietary knowledge — but building a RAG system that performs well in production requires far more than a basic retrieve-and-generate pipeline. This session goes beyond RAG fundamentals to cover advanced retrieval strategies, vector database architecture, and the design tradeoffs that determine whether a RAG system is accurate, fast, and scalable. Participants will explore chunking strategies, embedding model selection, hybrid search, re-ranking, and how different vector databases handle indexing and retrieval at scale — all through conceptual explanation, architecture diagrams, and real-world case studies.

🔗 Register now: LINK

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers who prefer a web browser to a terminal and want to launch nf-core pipelines on RCAC clusters through the Open OnDemand portal.

What You’ll Learn

• How to reach RCAC clusters through the Open OnDemand web portal
• How to set up inputs and parameters and launch an nf-core pipeline from the portal
• How to follow a run’s progress and find its results and reports from the browser

By the End of the Session, You’ll

• Launch an nf-core pipeline from Open OnDemand on an RCAC cluster
• Track a running pipeline and retrieve its outputs and reports
• Decide when the portal is enough and when to move to the command line

Level

Basic. No command-line experience required.

Register now: Click here to register

Description: The Model Context Protocol (MCP) provides a standardized way for AI applications and agents to connect with external tools, data sources, and services. This session will introduce the motivation behind MCP, its core concepts and architecture, and how it can be used to extend AI applications beyond simple text generation. We will also discuss where MCP fits within the broader ecosystem of AI agents, APIs, and retrieval-based applications.

Who should attend

Students, researchers, and developers interested in AI agents, LLM applications, or connecting language models to external tools and data.

What you'll learn

Participants will learn what MCP is, why it was developed, its main components, and how it can be used to connect AI applications with tools and external resources.

Level

Introductory to intermediate. Some familiarity with LLMs or APIs may be helpful but is not required. The session will primarily be lecture-based, with examples and demonstrations as appropriate.

Tags: AI 🔗 Register now: LINK

📅 Date: Tuesday, November 10, 2026
⏰ Time: 11:00 AM – 12:00 PM (EST)
💻 Location: Online (Microsoft Teams link provided upon registration)
🏫 Instructor: Arun Seetharam

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers whose analysis does not match an existing pipeline and who want to turn their own scripts into a reusable, shareable workflow.

What You’ll Learn

• How to break an existing shell, R, or Python script into Nextflow processes
• How to connect processes with channels and run many samples in parallel
• How to parameterize a workflow and package its software with modules or containers

By the End of the Session, You’ll

• Convert a linear script into a small, working Nextflow workflow
• Run that workflow across many samples with a single command
• Hand the workflow to a collaborator who can run it unchanged

Level

Intermediate. Sessions 3 and 4, or equivalent Nextflow experience, recommended.

Register now: Click here to register

As large language models grow in size and capability, deploying and customizing them efficiently has become a critical skill for AI practitioners. This workshop demystifies parameter-efficient finetuning (PEFT) and model compression techniques that make it possible to adapt and run LLMs without massive compute budgets. Participants will explore the theory and intuition behind LoRA and QLoRA, understand how quantization formats like GGUF enable local and edge deployment, and walk through real-world architecture and design tradeoffs.

Who Should Attend

ML/AI engineers and data scientists who want to understand model customization beyond prompting Software engineers building AI-powered products who need to reason about deployment cost and tradeoffs Researchers or students exploring efficient training methods for constrained hardware Anyone curious about how open-source LLMs are finetuned and compressed for local or edge use

Prerequisites: Basic familiarity with how neural networks and transformers work.

🔗 Register now: LINK

Please register using the link below to receive email reminders and workshop instructions; the “I’m interested” button does not provide access.

Description

A practical introduction to computing at RCAC for life-science researchers, covering how to get an account, what systems and software are available, and where to go for help. No prior HPC experience needed.

Who should attend

New faculty and postdocs who want to start using Purdue’s high-performance computing (HPC) systems for their life-science research.

What you’ll learn

• Understand how to get started on RCAC systems
• Know where to find documentation and support
• Be ready to run your first jobs or request help

Level

Introductory: For researchers new to HPC.

Registration: Register for Event

Please register using the link below to receive email reminders and workshop instructions; the “I’m interested” button does not provide access.

Registration Closes: 10/26/26 at 5 PM ET

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 for Event

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers who want production-quality pipelines for common assays such as RNA-seq and variant calling without writing them from scratch.

What You’ll Learn

TBD

By the End of the Session, You’ll

TBD

Level

Intermediate. Session 3 (Nextflow fundamentals) or equivalent experience is recommended.

Register now: Click here to register

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers who run multi-step analyses by hand or with shell scripts and want a workflow manager to handle scheduling, retries, and provenance for them.

What You’ll Learn

TBD

By the End of the Session, You’ll

TBD

Level

Intermediate. Comfort with the command line is expected; no prior Nextflow experience required.

Register now: Click here to register

Large language models can produce responses that appear useful, but evaluating their quality in a consistent and repeatable way can be challenging. This session will introduce practical approaches for evaluating LLMs and LLM-powered applications, including common metrics, task-specific evaluation, rubric-based scoring, and LLM-as-a-judge approaches. The session will also discuss how evaluations can help compare models, prompts, and application changes over time.

Who should attend

Students, researchers, and developers who use or build applications with large language models and want to better understand how to measure their performance and reliability.

What you'll learn

Participants will learn the fundamentals of LLM evaluation, common evaluation approaches and metrics, and how evaluations can be used to systematically compare and improve LLM-based systems.

Level

Introductory to intermediate. Basic familiarity with large language models is helpful but not required. The session will primarily be lecture-based, with examples and demonstrations as appropriate.

🔗 Register now: LINK

Who Should Attend Researchers, engineers, and students with basic programming or data science experience who want to expand their understanding of machine learning. This session is ideal for those working with real-world datasets who are interested in discovering hidden structures, patterns, or groupings in data without relying on labels.

What You’ll Learn This training introduces the fundamentals of unsupervised learning, where algorithms work with unlabeled data to uncover patterns and insights. You’ll learn about core methods such as clustering (e.g., k-means, hierarchical), and dimensionality reduction (e.g., PCA). The session will also cover practical applications like feature extraction, and data exploration. Key concepts such as choosing the right number of clusters, interpreting low-dimensional embeddings, and understanding limitations of unsupervised methods will be explained practically.

Level Beginner to intermediate

🔗 Register now: LINK

Please register using the link below to receive email reminders and workshop instructions; the “I’m interested” button does not provide access.

Registration Closes: 10/2/26 at 5 PM ET

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

Who Should Attend

Researchers, analysts, engineers, and students with basic programming or data science experience who want to build a solid foundation in machine learning. This session is ideal for anyone working with real-world datasets who wants to understand how supervised learning methods can be applied effectively in research, industry, or education.

What You'll Learn

This training introduces the fundamentals of supervised learning, the most widely used approach in modern machine learning. You'll learn how labeled data is used to train predictive models, explore the differences between regression and classification tasks. Core concepts like model evaluation, overfitting vs. underfitting, and performance metrics (e.g., accuracy, precision/recall) will be explained in practical terms.

Level

Beginner to intermediate

🔗 Register now: LINK

The Purdue chapter of Women in High Performance Computing invites you to an afternoon full of food and conversation!

Where Women in HPC Connect, Learn, and Lead

Women in HPC at Purdue is more than a chapter—it’s the backbone of something bigger. For the last three years, we've sponsored nearly 30 women to attend multiple conferences across the US. These events can be life changing experiences, helping the attendees to grow their network and learn about new career opportunities.

Aside from conference sponsorships, we've built mentorship circles where students meet PhDs and industry leaders. Purdue WHPC creates the kind of community that keeps women in the field instead of letting them quietly leave.

This is what that community looks like, gathered at one table.

You're invited to join the people who are building it.

Come for the connections, stay for the conversations that actually matter. Please join us on October 2, from 12:00 p.m. until 2 p.m. for our annual networking event. Lunch will be provided. Discussions often cover how to break into HPC, navigating your path, what it takes to stay and lead, and the real stories nobody tells until you're sitting face-to-face.

Whether you're a student finding your footing, a mentor looking for someone to invest in, a researcher building your next team, or a company looking for talent that's already passionate about this field, there's a place for you here.

"I came to Purdue not knowing anyone in HPC. I found WHPC, and suddenly I wasn't alone, I had mentors, a community, and a path forward." — WHPC community member

To attend, please register here: REGISTRATION

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

Who Should Attend

Researchers who generate or receive sequencing data and need to store, move, share, and describe it responsibly, including anyone writing a data management plan for a proposal.

What You’ll Learn

• Which RCAC storage service fits each stage of a project: scratch for active work, Depot for shared lab data, Fortress for long-term archive
• How to move large datasets reliably with Globus, between clusters and to and from collaborators and sequencing centers
• What reviewers look for in a data management plan, with ready-to-adapt DMP text that describes RCAC storage

By the End of the Session, You’ll

• Place your project’s data in the right storage service and explain why
• Transfer large datasets with Globus instead of fragile ad hoc copies
• Draft the storage and sharing sections of a data management plan using RCAC services

Level

Basic. Suitable for users at all experience levels.

Register now: Click here to register

Description: The AI landscape is evolving at an extraordinary pace, with new models, tools, frameworks, and deployment strategies emerging across both research and industry. AI Ecosystem 2026 provides a practical overview of the most important developments shaping the field and helps participants separate meaningful advances from short-lived trends. Rather than focusing only on individual model releases, this session looks at the broader ecosystem, including reasoning models, multimodal systems, open-source AI, agentic workflows, model infrastructure, and the growing role of AI in scientific and enterprise environments.

We will also examine how the AI stack is changing, from foundation models and inference platforms to orchestration tools and emerging standards. The goal is to give attendees a clearer understanding of where the field is heading, which technologies are becoming more important, and what organizations and researchers should be paying attention to as they plan future AI projects.

Who Should Attend Researchers, developers, data scientists, research software engineers, technical leaders, faculty, and professionals who want a current understanding of the rapidly changing AI ecosystem. This session is especially useful for those evaluating new AI technologies, designing AI-enabled workflows, or deciding which tools and trends are worth investing time in.

Topics

Major AI trends shaping 2026 Reasoning models and emerging model capabilities Multimodal AI across text, image, audio, and video Open-source versus proprietary AI ecosystems AI agents and agentic workflows Model orchestration, tooling, and development frameworks Inference, deployment, and infrastructure trends What matters most for research, enterprise, and scientific computing

Level Intermediate. Attendees should have a basic familiarity with AI and generative AI concepts, but deep technical expertise is not required.

Tags: AI 🔗 Register now: LINK

Please register using the link below to receive email reminders and the Microsoft Teams link; the “I’m interested” button does not provide access.

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