KEYNOTE SPEAKERS
Francisco Martínez Álvarez
Data Science & Big Data Lab, Pablo de Olavide University
Title: Quantum Machine Learning for Electrical Engineering and Electronics:
Foundations, Opportunities and Emerging Applications
Abstract:
Quantum computing is emerging as a new computational paradigm with the potential to address problems that remain challenging for classical approaches. Among its most promising areas, Quantum Machine Learning (QML) combines quantum computing with machine learning to explore new models for optimization, classification, pattern recognition, and high-dimensional data analysis. This keynote introduces the basic principles of quantum computing and QML and reviews the current technological landscape. The talk also addresses the limitations of current Noisy Intermediate-Scale Quantum devices and the role of hybrid quantum-classical approaches. The aim is to provide a realistic view of where QML stands today, which applications are most promising, and what opportunities it may create for future electrical and electronic systems.
Biography:
Francisco Martínez Álvarez is a Full Professor in the Computer Science Division at Pablo de Olavide University. He received his Degree in Telecommunication Engineering in 2005, his M.S. Degree in Computer Science in 2007, and his Ph.D. in Computer Science in 2010. His main research areas include time series, quantum machine learning, explainable artificial intelligence, and big data analytics, with over 200 published papers. He has supervised nine doctoral theses and led several national and international research projects. In 2015, together with Prof. Alicia Troncoso, he founded the Data Science & Big Data Lab. He has also served as principal investigator in numerous knowledge transfer projects in collaboration with more than 10 companies. Since 2013, he has held several management positions at Pablo de Olavide University. According to Stanford University’s analysis, he has been ranked among the world’s top 2% scientists for single-year impact every year since 2022, and for career-long impact from 2024.
Korhan Cengiz
College of Computing and Intelligent Systems, University of Khorfakkan, UAE
Title: Artificial Intelligence for Next Generation Telecommunication Systems
Abstract:
Telecommunication networks have evolved from voice services to systems that connect people, devices, and industries. This keynote examines how artificial intelligence (AI) is shaping that evolution, with a focus on its practical role in 5G and its expected place in 6G. It begins with the histories of telecommunications and AI, then shows how advances in both fields are coming together in modern networks.
In 5G, AI helps operators manage networks that must serve changing traffic demands and a wide range of applications. Machine learning can predict congestion, guide the allocation of bandwidth and spectrum, adjust beamforming, and balance traffic across network cells. AI can also detect faults, support maintenance, and reduce energy use when demand is low. These capabilities help improve service quality and make network operations more efficient.
The keynote connects these network functions to applications in smart cities, transportation, healthcare, industrial automation, and public safety. Examples in the presentation include connected factory operations, remote patient monitoring, traffic management, and emergency communications. They illustrate why reliable connectivity and timely analysis matter beyond the telecommunications sector.
Looking ahead, the talk presents 6G as a research vision in which AI may be integrated more deeply into communication systems. Topics include AI-native air interfaces, terahertz communication, integrated sensing and communication, digital twin networks, intelligent reflecting surfaces, and semantic communication. The presentation also explores potential applications such as extended reality, holographic communication, autonomous transportation, and human–machine interaction. These are future directions rather than established capabilities at commercial scale.
The keynote’s central message is that AI is changing how networks are planned and operated. Its current value lies in solving concrete 5G challenges, while 6G research asks how intelligence can become part of the network’s underlying design.