Active Projects

This goal of this project is to design a systematic solution for detecting and classifying anomalies in the distributed sustainable energy network with an explainable AI-based method. It will systematically and experimentally investigate several knotty issues in distributed systems, multi-modal learning, and explainability of AI. During the investigation period of the project, the following contributions can be made:

• Building a hierarchical learning framework that can process the different local conditions and heterogeneous cluster features in distributed sustainable energy equipment anomaly detection
• Proposing a multi-modal learning method utilizing various types of sustainable energy, aiming at improving the reliability of anomaly detection and classification with limited labeled data
• Developing an explainable AI (XAI) module to mitigate the black-box impact of AI models and facilitate human operation decision-making

The project outcome will advance the knowledge and understanding of multi-modal learning and XAI in the distributed energy system, and guide more AI-driven applications on crucial problems. The expected results will enrich education materials and strengthen curriculum development in the areas of distributed systems, multi-modal learning, anomaly detection, and XAI. Research outcomes will be widely disseminated online, shared at research seminars, and seamlessly integrated with K-12 education and outreach activities, engaging active participation from underrepresented student groups. This project will allow the PI to develop a well-established long-term collaboration with national prominence institution. Therefore, this project will significantly empower the PI to become a competitive researcher and a qualified educator in his future career, enhance the research capacity of the PI's home institution, and develop a diverse workforce for the PI's jurisdiction.

The project has three specific aims: (1) to build immersive learning experiences for the rapidly growing student interest in generative artificial intelligence, (2) to enable computation-intensive education and research across disciplines, and (3) to address long-term workforce development needs in generative artificial intelligence fields. By providing modern computational resources, this GPU cluster will support hands-on learning for students, allowing them to work with cutting-edge generative artificial intelligence techniques, including generative adversarial networks, diffusion models, and large language models.

The research methods include integrating the GPU cluster into coursework, utilizing state-of-the-art generative artificial intelligence algorithms and deep learning frameworks, enabling projects that involve processing high-dimensional image or text datasets. Creating computational and extensive interdisciplinary collaborations. Expected outcomes will increase student engagement and expertise in a broad area of artificial intelligence.

Also, this project will produce new courses for both undergraduate and graduate students and stimulate growth in course enrollments as students gain access to resources that significantly enhance their learning and practical experience. Additionally, this whole project will prepare students with adequate experience as the future workforce in different domains of generative artificial intelligence careers. Results from this project will be disseminated through academic publications, community outreach, and expanded partnerships with local and regional institutions. As the only R1 university in southern Nevada, establishing this infrastructure at UNLV will empower underrepresented students with competitive skills, address existing disparities, and advance educational equity. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.