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Introduction to Machine Learning and AI with Python on the HPC

Faculty: David C. Anastasiu, Assistant Professor, Computer Science and Engineering

Project Overview: This workshop was designed to provide a broad entry point for users looking to harness the power of Python-based artificial intelligence within a high-performance computing environment. The curriculum focuses on fundamental machine learning concepts and demonstrates how to scale these workflows using the WAVE HPC's computational resources.

Impact & Outcomes: The course is currently hosted on Camino () and has been completed by 55 users, including students, faculty, and staff. Due to its foundational value, it has been integrated into the HPC Center website and will be utilized as a core training component for the undergraduate Machine Learning and Data Mining (CSEN 140) course in Spring 2026 and other AI/ML CSEN graduate courses including Machine Learning (CSEN 240), Data Mining (CSEN 281), and Deep Learning (342).

HPC Essentials for Business Faculty

Faculty: Haibing Lu, Professor, Information Systems and Analytics

Project Overview: Professor Lu developed this online, asynchronous tutorial specifically to help business scholars and graduate students manage data- and compute-intensive problems. The course includes specialized modules on building a Semantic Paper Recommender System using vector embeddings and an Image Similarity Search System modeled after commercial AI architectures like Amazon StyleSnap.

Impact & Outcomes: Currently available on Camino (), the tutorial has been integrated into the MSIS Industry Practicum (MSIS 2540 & 2542). It has already prepared students for high-level research and corporate projects involving Large Language Models (LLMs) with partners such as AWS, Adobe, and Ernst & Young (EY).

Introduction to High Performance Computing

Faculty: David C. Anastasiu, Associate Professor, Computer Science and Engineering

Project Overview: This workshop is being developed to serve as the definitive onboarding resource for new WAVE HPC users. The curriculum will cover essential topics including Linux environment navigation, efficient file storage management, the use of software modules, and the basics of job scheduling and submission using Slurm.

Leveraging Jupyter Notebooks to Introduce Computational Skills to Non-CS Students

Faculty: Michelle McCully, Associate Professor, Biology

Project Overview: Designed specifically for life sciences students who may lack a computer science background, this workshop introduces Python-based computational skills through an accessible Jupyter Notebook interface. The goal is to provide modular, guided tutorials that allow students to analyze biological data without the steep learning curve often associated with traditional command-line environments.

Using the WAVE HPC for Time Series Forecasting (TSF)

Faculty: Navid Shaghaghi, Lecturer, Computer Science and Engineering

Project Overview: This advanced workshop focuses on the complexities of Time Series Forecasting, teaching users how to build and train models such as ARIMA, Prophet, and LSTMs. Students will learn how to leverage the WAVE HPC’s GPU nodes to accelerate the training of these models for applications in finance, weather prediction, and resource management.

Efficiency-Driven Command Line Skills for High-Performance Computing

Faculty: Robin Grotjahn, Assistant Professor, Chemistry and Biochemistry

Project Overview: To help users maximize their productivity, this course focuses on advanced Bash command-line proficiency. The curriculum covers the creation of customized aliases, the use of chained commands to automate repetitive tasks, and workflow automation strategies that ensure research reproducibility and technical efficiency.

Hands-On HPC Workshop for Realistic Wireless System Modeling using NVIDIA Sionna

Faculty: Krishna Ramamoorthy, Assistant Professor, Department of Computer Science and Engineering

Project Overview: This workshop bridges theoretical wireless modeling and practical evaluation by guiding participants through GPU-accelerated simulations using NVIDIA Sionna. Modular lab exercises will cover building a digital twin of the SCU campus via ray tracing, designing AI-native receivers, and utilizing reinforcement learning for adaptive link optimization.

Intelligence at Your Fingertips: Local and Remote LLM Integration via Jupyter AI

Faculty: David C. Anastasiu, Associate Professor, Computer Science and Engineering

Project Overview: This initiative provides an educational framework for researchers to access both remote and local Large Language Models (LLMs) directly within Jupyter AI research notebooks. Participants will learn to leverage SCU’s GPU resources for high-performance inference using a centralized "Model Zoo" while maintaining data privacy and exploring hardware-aware automation for diverse research domains.

Managing Undergraduate Research and Courses on WAVE HPC

Faculty: Dr. Robin Grotjahn, Assistant Professor, Chemistry & Biochemistry

Project Overview: This hands-on workshop provides faculty and advanced student mentors with the operational skills needed to structure and maintain shared research group or course-based workspaces on the WAVE HPC. The curriculum includes practical training on directory organization, file permission management, local software installation, and the use of shell scripts to automate recurring lab tasks.