Teams

Project Goals

• Understand local open-source tools, OS, automations, and ethical computing vs digital colonialism
• Tour of useful open source data skills via JupyterLab, Metabase, Python for Environmental Justice analysis
• Critical analysis of big tech power and building confidence to utilize open source solutions

Project Goals

• Integrate science gateway resources into teaching and research activities
• Expand student access to computing tools, data, and hands-on learning
• Provide faculty and students with training and technical support

Project Goals

• Increase access to computing resources with Jetstream2 virtual computing environments
• Integrate generative AI into programming instruction for code generation, debugging, explanation, and problem solving
• Develop authentic computational projects using Jetstream2 and AI that mirror real-world computing applications

Project Goals

• Introduce students to Programming with HPC Perspectives
• Explore resources (e.g., data sets) with a view to produce reproducible projects
• Train students to integrate and visualize results from science gateway platforms

Science Gateways Resources

• Jetstream2
• Cornell Virtual Workshop
• HPC Carpentry

Project Goals

• Understand the concept of Computer Architecture
• Integrate the concept and understanding of cloud architecture as it relates to traditional computer architecture
• Analyze how computer architecture principles support artificial intelligence workloads by examining the interaction between processors, memory systems, storage, parallel computing, and specialized AI hardware accelerators
• Prepare students to understand the importance of computer architecture in traditional on-prem and modern cloud computing models in IaaS, PaaS, SaaS

Project Goals

• Introduce entry-level IoT students to science gateways, public datasets, Jetstream 2, Jupyter notebooks, cloud computing, and HPC resources
• Develop foundational notebook and visualization skills through scaffolded activities designed for students with no prior Python or HPC experience
• Help students run prepared code, modify selected variables, visualize authentic IoT and industrial data, and interpret the results
• Introduce AI-assisted anomaly detection, cybersecurity, and critical infrastructure applications in an approachable manner
• Guide students in transferring these skills to a sensor-based final project in which they collect, analyze, visualize, and report on their own data

Project Goals

• Develop a beginner-friendly CURE module in Ecology 404B that guides biology majors through ecological question development, data exploration, visualization, and interpretation
• Use Louisiana-focused ecological and environmental datasets to connect course concepts to wetlands, biodiversity, climate change, water quality, and coastal change
• Build student confidence with computational ecology through guided CloudBank/Jupyter notebook activities, structured tutorials, and reflection checkpoints

Project Goals

• Reinforce economic concepts by applying microeconomic and macroeconomic theories to practical public-sector problems
• Develop analytical skills by using equations, data, and quantitative methods to analyze economic relationships and policy choices
• Build R proficiency by using R for data analysis, computation, and graphical visualization
• Integrate AI responsibly by using LLMs such as SGX2 Gateways LLMs, Claude, and Codex to support coding, research, and problem-solving while critically evaluating AI-generated outputs
• Apply real-world data by working with authentic datasets to connect economic theory with evidence-based public policy and decision-making

Science Gateways Resources

• Jetstream2
• CloudBank
• SGX3/SGX2 Gateways LLMs
• Claude
• Codex
• ORNL Datasets
• Science Gateways Resource Browser

Project Goals

• Develop four scaffolded CURE activities centered on urban heat and environmental resilience in Baton Rouge and South Louisiana
• Guide students through Discovery, Visualization, Analysis, and Interpretation using gateway datasets and student-collected field data
• Use HPC Carpentry's Introduction to HPC to prepare students for shared computing, remote resources, and responsible data practices
• Produce reusable notebooks and authentic products: a research report, accessible poster, and community-facing environmental brief

Project Goals

• Evaluate open-weight large language models (LLMs) that students can deploy, fine-tune, and benchmark on HPC systems through science gateway interfaces
• Curate domain-relevant datasets and pre-configured computing environments (Jupyter/containerized) that can be published and reused on science gateways
• Map gateway-hosted datasets across domains (environmental, health, geospatial) to specific course modules — clustering, computer vision, NLP — creating a reusable crosswalk that shows instructors which gateway data assets support which AI competencies

Science Gateways Resources

• National Research Platform (NRP)
• Cornell Virtual Workshop
• HPC Carpentry