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Parallel Computing (CSEN 145)

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

Project Overview: This core engineering course was redesigned to keep students trained on the latest parallel programming frameworks that require high performance computing environments. The update introduced frameworks such as AMD ROCm and $C++20$ STL parallelism while expanding coverage of GPU programming with CUDA and OpenACC.

Impact & Outcomes: Offered in Fall 2024, 20 students learned to use the WAVE HPC for all class assignments and labs. Students gained a practical understanding of different parallel architectures—including AMD EPYC CPUs and MI-100 GPUs—enabling them to choose appropriate models for real-world projects.

Molecular Modeling (BIOL 172)

Faculty: Michelle McCully, Associate Professor, Biology

Project Overview: This redesign focused on bringing Python programming into the life sciences context for building and analyzing molecular models. Key updates included incorporating the robust MDAnalysis library for molecular dynamics (MD) simulations and developing new activities where students code their own drug-docking algorithms.

Impact & Outcomes: In Winter 2025, 16 students from various majors (Biology, Neuroscience, Biochemistry, and Bioengineering) used Jupyter Notebooks on the HPC to analyze the dynamics of SARS-CoV-2 protease. Students reported a significant increase in computational confidence, noting the reward of seeing their own code come to fruition.

Climate Change: Past to Future (ENVS 166)

Faculty: Will Rush, Assistant Professor, Environmental Studies and Sciences

Project Overview: Professor Rush added a central computational component to this upper-division lab course to educate students in big-data analysis and HPC usage. The project involved developing modules for Python coding, visualization of historical records, and the construction of interactive 1D and General Circulation Models (GCM) of the atmosphere.

Impact & Outcomes: During the Winter 2025 quarter, 33 students utilized the HPC to characterize the climate of specific global regions. The course serves as a critical pipeline for recruiting research assistants by improving student confidence in engaging with complex climate datasets.

Industry Practicum (MSIS 2540 & 2542)

Faculty: Haibing Lu, Professor, Information Systems and Analytics

Project Overview: This project developed a comprehensive, asynchronous tutorial for graduate students to leverage WAVE HPC resources for computationally intensive industry projects. Two specific modules were integrated: a Semantic Paper Recommender System using vector embeddings and an Image Similarity Search System based on the CLIP vision-language model.

Impact & Outcomes: Integrated in Winter 2025, graduate students used the tutorial to prepare for real-world projects with sponsors like AWS, Adobe, and Ernst & Young (EY). These practicum projects involved training sophisticated machine learning models and fine-tuning large language models that required resources beyond typical classroom tools.

Deep Learning (CSEN 342)

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

Project Overview: This proposal fully redesigns the existing Deep Learning course to integrate structured hands-on learning for frameworks such as PyTorch and TensorFlow. Each lecture will be accompanied by a tutorial or coding activity that bridges the gap between theory and practical model implementation. Students will gain experience running workloads on the WAVE HPC, including GPU-based training and utilizing Slurm for job scheduling.

Computational Chemistry (CHEM 155)

Faculty: Robin Grotjahn, Assistant Professor, Chemistry and Biochemistry

Project Overview: This new course introduces undergraduate students to high-performance computing fundamentals applied to density functional theory (DFT) calculations. Students will learn Linux navigation, Bash scripting, and job scheduling using SLURM. Using the Turbomole software on the WAVE HPC, the class will work on large, real-world molecules to produce original research contributions to ongoing scientific manuscripts.

Deep Learning for Visual Coding (CSEN 396B)

Faculty: Ying Liu, Associate Professor, Computer Science and Engineering

Project Overview: This new graduate-level course covers AI-driven image and video compression, generative adversarial networks (GANs) for coding, and vision-language models. The course bridges the gap between traditional compression methods and state-of-the-art AI technologies. Students will form groups to work on mini-projects involving the code implementation, training, and testing of deep neural networks using WAVE HPC GPU servers.

Bioinformatics (BIOL 178)

Faculty: Justen Whittall, Professor, Biology

Project Overview: This comprehensive redesign moves the Bioinformatics curriculum away from personal laptops to harness the power of the HPC. The update will allow students to increase the scale of DNA sequences analyzed from small batches to thousands. Students will utilize sophisticated computational methods—including Maximum Likelihood, Bayesian approaches, and machine learning techniques—using industry-standard tools like BLAST, Bowtie, and GATK.

AI for Customer Analytics (MKTG 174)

Faculty: Shunyao Yan, Assistant Professor, Marketing

Project Overview: This proposal revamps the Leavey School of Business course to address the insufficient power of standard laptops for training advanced AI models on large marketing datasets. The redesign introduces HPC-oriented modules for model training and big data wrangling using R/RStudio and JupyterLab. Students will learn to apply high-performance computing to tasks like Natural Language Processing (NLP) of massive social media streams and uplift modeling for campaign management.

Information Systems and Business Analytics Industry Practicum (ISBA 2700)

Faculty: Jingbo Hou, Assistant Professor, Information Systems & Analytics

Project Overview: This proposal transforms a two-quarter experiential practicum into an HPC-enabled course focused on analyzing real-world financial technology systems. Students will utilize campus HPC resources for large-scale data engineering, distributed machine learning, and ethical stress-testing of algorithmic decision-making platforms.

Quantum Chemistry (CHEM-151)

Faculty: Robin Grotjahn, Assistant Professor, Chemistry and Biochemistry

Project Overview: This proposal integrates WAVE-HPC into an upper-division chemistry course to enable high-accuracy Density Functional Theory (DFT) calculations that were previously restricted by the limitations of commercial web platforms. Students will learn to navigate HPC environments via SSH and Slurm to perform sophisticated molecular simulations and compare computational predictions with experimental spectroscopic data.

Introduction to Latina/o/x & Chicana/o/x Studies (ETHN 20)

Faculty: Jesica Siham Fernández, Associate Professor, Ethnic Studies

Project Overview: This project redesigns a core module to transition students from traditional qualitative "close reading" to large-scale computational analysis of over 10,000 documents. By leveraging HPC for Natural Language Processing and parallel text-mining, students will learn to identify systemic biases and power structures within digital media representations of marginalized communities.

Advanced Deep Learning for Generative AI (New Course, CSEN 362)

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

Project Overview: This project introduces a new, advanced engineering course dedicated to the theory and implementation of massive architectures like Transformers and Diffusion models. Students will gain hands-on proficiency in HPC environments by utilizing Slurm scheduling, multi-GPU distributed training, and resource-aware optimization techniques such as LoRA and quantization.

Human-AI Collaboration in Finance (New Course)

Faculty: Alexander K. Zentefis, Assistant Professor, Finance

Project Overview: This project establishes a new interdisciplinary course training students to collaborate with AI systems in financial institutions using datasets that exceed standard laptop capacity. Students will utilize HPC resources to execute machine learning models and compute-intensive explainability tools like SHAP and LIME to detect algorithmic bias in lending and fraud detection.