Special Session 3. Generative AI and Real-World Applications

 1. Brief Description

Generative Artificial Intelligence (GenAI) is rapidly transforming research, industry, and society through advanced capabilities in content generation, reasoning, automation, and multimodal understanding. This special session aims to bring together researchers, practitioners, and educators to discuss recent advances, challenges, and real-world applications of Generative AI technologies. Topics of interest include Large Language Models (LLMs), Vision-Language Models (VLMs), multimodal systems, AI agents, Human-In-The-Loop collaboration, and domain-specific applications. The session also welcomes contributions addressing including explainability, ethical considerations, privacy, and edge deployment of lightweight generative models. By promoting interdisciplinary discussions, this special session seeks to highlight innovative methodologies, practical implementations, and emerging research directions that advance the development and responsible adoption of Generative AI systems.


 2. Special Session Chair

Assoc. Prof. Edgar Eduardo Ceh-Varela, Eastern New Mexico University (ENMU), USA


 3. Special Session Topics

Potential topics of interest include but are not limited to:
▪ Large Language Models (LLMs), Vision-Language Models (VLMs) and Multimodal AI
▪ Generative AI in Education and Intelligent Tutoring Systems
▪ Generative AI in Medicine and Healthcare Applications
▪ Generative AI in Real-World Industrial Applications
▪ Human-AI Collaboration and Human-in-the-Loop AI
▪ Explainable, Ethical, and Trustworthy Generative AI
▪ Edge and Lightweight Generative AI Systems
▪ Generative AI for Software Engineering and Code Generation
▪ Generative AI for Cybersecurity and Threat Detection


 4. Introduction of Special Session Chair

Assoc. Prof. Edgar Eduardo Ceh-Varela, Eastern New Mexico University (ENMU), USA

Dr. Edgar Eduardo Ceh-Varela is an Associate Professor of Computer Science at Eastern New Mexico University (ENMU), USA. He obtained his Ph.D. in Computer Science in 2021 from New Mexico State University (NMSU). He also has a Doctorate in Computer Systems from Universidad del Sur, in Mexico. His research topics include Applied Machine Learning, NLP, Recommender Systems, and currently Agentic AI. Because of these areas of research, he was the coordinator of the AI Research and Development subcommittee for ENMU. He has several research articles and book chapters in international venues. Currently, he directs the “Emerging Machine-learning Modeling and Analysis” (EMMA) lab, where his students work on multiple AI research projects that have won local and state awards. For this effort he is the recipient of the 2025 NM EPSCoR Outstanding Mentor Award and the 2025-2026 ENMU Presidential Award for Excellence in Research, Scholarship, and Creative Activity.