Abstract : This talk provides an in-depth exploration into resource management within 6G wireless networks, focusing on the vision, key performance indicators (KPIs), key enabling techniques (KETs), and the diverse array of services characteristic of these advanced networks. The distinct challenges inherent in 6G’s resource management call for a pivotal shift towards artificial intelligence (AI) and machine learning (ML)-driven solutions, necessitating a departure from traditional optimization-centric approaches. This talk will shed light on generative AI and unsupervised ML strategies tailored to effectively address convex and non-convex resource management optimization problems. A key focus will be on deep unsupervised learning techniques for network resource allocation, addressing non-linear and non-convex constraints. Deep implicit layers and differentiable projection methods will be explored as mechanisms to ensure zero constraint violations in applications such as beamforming, phase-shift optimization, and power allocation. Furthermore, the potential of generative AI models, including large language models (LLMs), to enable proactive network resource allocation will be examined, highlighting their role in optimizing performance and reducing reliance on traditional heuristics. The session will conclude by identifying key research gaps and future directions, paving the way for next-generation AI-driven wireless networks. Co-sponsored by: Reza Soleymani Speaker(s): , Dr. Tabassum Room: EV003-309, Bldg: Electrical & Computer Engineering Department, 1515 Ste. Catherine West (corner with Guy St.), Concordia University, Montreal, Quebec, Canada, H3G 1M8