Machine learning and data-driven solutions for cost-efficient network automation

Sabidur Rahman
Department of Computer Science, Sonoma State University

Stevenson Hall 1300
12:00 PM - 12:50 PM

Machine Learning (ML) and data-driven solutions have revolutionized many areas of technologies. Communication technology is also increasingly benefiting from such solutions. Automated network resource management powered by ML and data-driven solutions can help to reduce the cost of connectivity, to free up more bandwidths, to foster innovation on the connected services etc., leading to more connected society and businesses. Many time-consuming and complex tasks of network resource management are being automated; thanks to virtualization of network components, advancements in artificial intelligence, and insights learned from data. Dr. Sabidur's research works with Networks Research Labs at UC Davis and AT&T Labs explore important problems in this area of research. This is an exciting new area of research with potential impact on Edge Computing, IoT, Machine Intelligence, Industry 4.0, Smart City, 6G and beyond.