Registration link: https://purdue.ca1.qualtrics.com/jfe/form/SV_572GkrN6SSeYJSe

Registration link: https://purdue.ca1.qualtrics.com/jfe/form/SV_5cJvPnJax6JjLpk

Registration Link: https://purdue.ca1.qualtrics.com/jfe/form/SV_af1VNhe7NurYvpY

Registration link: https://purdue.ca1.qualtrics.com/jfe/form/SV_af1VNhe7NurYvpY

Registration Link: https://purdue.ca1.qualtrics.com/jfe/form/SV_9HqQtOK6kw9FGf4

About the Camp Your student will step into the role of Mission Control during a simulated Mars mission in crisis! Through three interconnected, story-driven STEM challenges, campers will investigate real problems, design creative solutions, and collaborate with peers - all in a race to save the mission.

This is a full-day, hands-on experience that brings science, technology, engineering, and math to life in a way no classroom can.

Three One-Day Sessions Available

• Session 1 - June 15, 2026

• Session 2 - June 16, 2026

• Session 3 - June 17, 2026

Each session runs from 9:00 AM – 4:00 PM. Snacks and lunch are included.

COST: $50 per camper | Financial aid may be available - please inquire.

Bonus Tours As part of the camp experience, students will also enjoy a guided tour of Purdue's Research Computing Center, including:

• The Data Center - home to Purdue's powerful supercomputing systems

• The Envision Center - where researchers use virtual and augmented reality for cutting-edge data visualization

This behind-the-scenes look at real research technology is a unique opportunity students won't find anywhere else!

Spaces are limited - register today to secure your student's spot!

REGISTER HERE

Registration link: https://purdue.ca1.qualtrics.com/jfe/form/SV_cNrr2Kpb2tS9ifQ

Registration Link: https://purdue.ca1.qualtrics.com/jfe/form/SV_88lX5O7hAfLyqzQ

This session introduces the foundations of modern AI and explains how agentic workflows are built. We will explore how foundation models serve as reasoning engines, how tools and APIs extend their capabilities, how memory enables context retention, and how control loops orchestrate planning and execution. Participants will gain both a conceptual clarity and practical insight into how agentic systems are engineered in real-world environments.

Register Here: https://purdue.ca1.qualtrics.com/jfe/form/SV_038TucaSL7hZvJI

Register here: https://purdue.ca1.qualtrics.com/jfe/form/SV_8uz1ktjhkSyk7dk

Faculty, staff, and students are invited to present or attend!

Event: May 22, 2026 @ 10am EDT

Speakers:

Sasmita Mohapatra – HPC Research Scientist & Facilitator, University of Texas at Dallas
Dr. Sasmita Mohapatra is a HPC Research Scientist and facilitator working on HPC software tools and user support at the University of Texas at Dallas (UTD), High Performance Computing, Office of Research and Innovations, in Richardson, Texas. She works on parallel jobs, SLURM configuration, and resource usage analysis. She’s been focused on improving job efficiency policies across their clusters. She enjoys debugging weird MPI behavior, parallelizing code with OpenMP and performance tuning with OpenMP and MPI. She also enjoys helping users get the most out of their code. She is actively involved in HPC orientations, user guide and several other training programs for UTD clusters.

Anjusree Karnavar - Doctoral Researcher at Griffith University, Queensland , Australia, working in collaboration with the Commonwealth Scientific and Industrial Research Organization (CSIRO)
Karnavar's research focuses on Computer Vision, Image Restoration, and Deep Learning, developing advanced AI techniques, using diffusion models and Vision Transformers, to restore and enhance degraded images for more reliable vision-based systems. Before pursuing her PhD, she worked for six years as an Assistant Professor in Computer Science and Engineering at APJ Abdul Kalam Technological University, India. Her interests lie in advancing AI-driven methods for visual computing and intelligent systems.

To attend, please register here: REGISTRATION

Presentation Callout

If you would like to present at our May 22 event, or any future events, please fill out the interest form: Presenter Proposal Form

Information for Presenters:

• Presenter proposals due: May 15, 11:59 PM EDT (Fast Approaching!)
• Presentation formats: Lightning Talk, Standard, or Extended
• Proposal: 2–5 sentence description of your talk

Choose between a:

• Lightning Talk (5-10mins)
• Standard Presentation (20-30mins)
• Extended Presentation (45mins)

Why present?

• Share your work with WHPC
• Practice presenting in a supportive space
• Shape the future of WHPC technical programming

This opportunity is open to all experience levels—first-time presenters encouraged! Please feel free to share this page with colleagues who may be interested in attending or presenting!

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)

Description  AI is creating new opportunities across both scientific research and education, but it also introduces governance challenges that institutions must address thoughtfully. This session examines how AI can support research discovery, analysis, drafting, teaching, tutoring, assessment, and administrative workflows, while also highlighting the risks related to integrity, confidentiality, privacy, learner outcomes, and institutional trust. Rather than treating research and education as the same problem, this workshop explores how each domain has distinct governance needs, along with a shared need for transparent accountability and evidence-based oversight. Through policy context, practical governance tools, and real-world case studies, we will discuss how institutions can enable innovation while protecting academic values and public trust.

Who Should Attend  Faculty, researchers, academic technologists, research computing professionals, instructional staff, administrators, library and IT leaders, graduate students, and others involved in supporting or governing AI use in research and educational settings. This session is particularly relevant for those developing institutional guidance, evaluating AI-enabled workflows, or trying to balance innovation with integrity and privacy.

Topics  Where AI can add value in scientific research and education  The distinct governance challenges in research and in teaching and learning environments  Core risks such as fabricated content, reproducibility issues, confidentiality concerns, privacy risks, over-reliance, and unequal access  The evolving policy landscape, including UNESCO guidance, education quality signals, funder expectations, publisher norms, and data protection considerations  Governance frameworks for lifecycle review, risk tiering, and evidence-based oversight  Practical toolkits for research reproducibility, documentation, disclosure, academic integrity, and assessment redesign  Institutional roles, decision rights, monitoring practices, and leadership metrics  Lessons from case studies involving AlphaFold, Khanmigo, AI detection limits, and paper mills

Level  Intermediate. Attendees should be broadly familiar with AI concepts, but no specialized expertise in governance, policy, or advanced technical systems is required. 🔗 Register now:LINK

What You'll Learn: This training introduces AutoML (Automated Machine Learning) as a framework and paradigm for automating key steps in building machine learning models, including model selection, hyperparameter tuning, and evaluation. Participants will learn the core ideas behind how AutoML frameworks operate, see how they can quickly produce strong baseline models, and be briefly introduced to representative implementations such as Auto-sklearn. At the same time, the training emphasizes important limitations—such as reduced control, risk of overfitting, computational cost, and limited transparency—so learners leave with both the confidence to use AutoML effectively and the judgment to know when a more hands-on approach is the better choice.

Who Should Attend: Anyone who wants to build effective machine learning models quickly using automated approaches, while understanding when and why to go beyond them.

Level: Intermediate level, suitable for those with basic familiarity with machine learning concepts but no prior experience with AutoML.

🔗 Register now:LINK

This seminar series brings Purdue faculty and researchers together to explore AI opportunities and build connections across campus and NSF-supported programs. Each session features two 20-minute faculty talks followed by 20 minutes of Q&A and networking. In each session, you will:

• Hear short talks showcasing AI work across diverse disciplines
• Meet and network with faculty interested in AI research and teaching
• Share your challenges and expectations with Purdue AI Hubs admins.
• Participate in open discussion and Q&A
• Learn about Purdue and NSF AI, computing, and data services
• Join the conversation and help shape the future of AI at Purdue.

The series is open to anyone at the university and registration is not required. Please join us in helping to advance AI at Purdue!

Schedule:

All sessions take place from 3-4 p.m. in DSAI 1069

• Februray 10: Professor Nikhilesh Chawla (Materials Engineering), Professor Alexandra Boltasseva (Electrical and Computer Engineering)
• March 3: Professor Yan Gu (Mechanical Engineering), Professor Jinha Jung (Civil and Construction Engineering)
• March 10: Professor D. Marshall Porterfield (Agricultural and Biological Engineering), Professor Yaguang Zhang (Agricultural and Biological Engineering)
• March 24: Professor Vanesa Cañete Jurado (College of Liberal Arts), Professor Maurice Tetne (College of Liberal Arts)
• April 7: Professor Edwin Garcia (Materials Engineering), Professor Romit Maulik (Mechanical Engineering)
• April 21: Professor Prianka Baloni (Health Sciences), Professor Mia Liu (Physics and Astronomy)
• April 28: Professor Mustafa Abdallah (Computer and Information Technology), Professor Rua M. Williams (Applied and Creative Computing)

Details of talks will be published the week before each event through RCAC and IPAI mailing list. Please subscribe to our RCAC newsletter and our IPAI newsletter for more information.

Description As AI adoption accelerates across organizations, governance can no longer rely on static policies, one-time reviews, or high-level principles alone. Responsible AI & Governance 2.0 focuses on how institutions can move toward a more mature operating model built on continuous evidence, lifecycle controls, accountability, and auditable processes. This session explores how modern AI governance is evolving in response to changing regulatory expectations, decentralised AI adoption, and growing pressure to deploy systems quickly without compromising safety, trust, or compliance. Drawing on practical governance frameworks, standards, and real-world case studies, we will discuss how to design governance processes that are risk-based, scalable, and usable in real environments.

Who Should Attend AI/ML leaders, technical directors, platform and product owners, data scientists, risk and compliance professionals, security and privacy staff, research software engineers, and institutional decision-makers responsible for evaluating, deploying, or overseeing AI systems. This session is especially useful for those who want to understand how to operationalize trustworthy AI beyond policy statements and into real governance workflows.

Topics What “Governance 2.0” means and how it differs from traditional governance models The main pressures shaping AI governance today, including regulation, delivery speed, risk, and trust Trustworthy AI dimensions such as validity, safety, security, accountability, explainability, and fairness How generative AI changes the governance risk surface Risk-based governance approaches, including intake, classification, routing, and review Documentation, testing, release gates, monitoring, and incident response as part of an evidence-driven governance model Third-party and foundation model governance, including vendor risk and change management Implementation roadmaps, leadership metrics, and common governance pitfalls to avoid

Level Intermediate. Attendees should have a basic understanding of AI systems and organizational technology use, but no prior background in formal AI governance frameworks is required. 🔗 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

• Host: Purdue RCAC, Envision Center
• When: Monday, April 20 | 2:30 PM – 4:30 PM
• Where: WALC 2124
• Other information: No registration needed

Overview: The XR Forum will serve as a community discussion space for faculty and researchers to share and learn on topics of XR in academia. As a continuation from previous XR symposiums, this event is designed to bring researchers together to share their knowledge, best practices, lessons learned, and pain points to further improve the experience in leveraging immersive technologies. This forum is also intended to help connect researchers to existing resources and establish opportunities to connect researchers with common goals.

Topics:

• Hardware, support, sustainability and resources
• Consumer & enterprise equipment/resource landscape
• XR Research and IRB
• Collaborative opportunities and Grants
• Demos & Discussions

Agenda:

• 2:30-3:00 Arrival, Welcome, and Introductions
• 3:00-4:00 Forum discussion topics
• 4:00-4:30 Wrap up, break-out discussions and demos

• Host: Purdue RCAC, Center for Research Software Engineering (RSE)

• When: Friday, April 17 | 9:00 AM – 12:30 PM

• Where: STEW 202

• For more information, please visit: https://www.cyberfaces.org/learn/course/351-streamci-spring-workshop-

• Other information: Breakfast pastries, coffee, tea, and lunch will be provided

• Registration Link and Deadline: April 3rd, 2026 https://purdue.ca1.qualtrics.com/jfe/form/SV_4VDLOdrxDAlxaia

Workshop Overview: The StreamCI Spring Workshop will bring together researchers, research software engineers, and cyberinfrastructure partners to explore how StreamCI, a real-time sensor data management and processing infrastructure, can accelerate research, enable new analytical workflows, and support scalable, reproducible science. Funded by the NSF CSSI program, StreamCI is an AI-ready streaming data platform designed to support diverse scientific applications. It addresses the need to significantly lower the barrier for domain scientists to manage their sensor data and develop streaming data–driven ML/AI models, analyses, and applications. This workshop will highlight StreamCI’s capabilities and opportunities for collaboration. The team will also provide a live demonstration of the platform, and faculty researchers will present active science use cases that illustrate how StreamCI supports their data-driven research.

Intended Outcomes for participants:

• Gain clarity on how StreamCI can support their research
• Identify potential pilot projects or collaborative opportunities in the future
• Connect with RSE and cyberinfrastructure partners at Purdue

Agenda:

• 9:00-9:15 Arrival, Coffee & Informal Networking
• 9:15-9:30 Welcome, Workshop Framing, and Use Cases Overview
• 9:30-10:30 SteamCI Architecture and Capabilities. Live Demo
• 10:30-11:30 Faculty Research Highlights & Data Use Cases and use case demos

1. UC1 - Energy Sustainability
2. UC2 - Crop health and pesticide control
3. UC3 - Precision audiology
4. UC4 – Pavement Monitoring

• 11:30-12:30 QandA, Remarks, working session
• 12:00-12:30 Lunch and networking

This beginner-friendly technical session is designed for anyone with basic Python knowledge who wants to explore how deep learning is transforming industries like healthcare, retail, and automotive. Participants will learn the fundamentals of training models using PyTorch, understand key architectures such as CNNs and RNNs, and explore techniques like data augmentation and transfer learning. Through practical examples in computer vision and natural language processing, attendees will gain the confidence to start building their own deep learning projects. 🔗 Register now:LINK