Research team
Expertise
My research experience focuses on the intelligent management and orchestration of computing and network resources in distributed edge, cloud, and 5G/6G environments. My work explores how artificial intelligence can be considered a first-rate orchestrable capability, enabling the automated placement, scaling, migration, model selection, and lifecycle management of AI applications and components. My main research topics include AI-driven orchestration, multi-access edge computing, network intelligence, network digital twins, reinforcement learning, adaptive autoscaling, Kubernetes-based infrastructures, and MLOps. I investigate the trade-offs between latency, power consumption, resource utilization, service-level objectives, and machine learning accuracy. My research activities include designing and evaluating reinforcement learning controllers, comparing them with threshold-based and control theory approaches, developing digital twin environments for experimentation, and building closed-loop monitoring and orchestration pipelines using Kubernetes, Prometheus, Grafana, Kepler, Prefect, and containerized microservices.