KEYNOTES
Ana García Armada
Towards the Internet of Senses
This talk examines the evolution of mobile communications towards a future system that would be able to provide fully immersive communications that can convey all human senses. It does so by exploring the experience gained in several collaborative projects, covering from virtual reality to remote teleoperation, looking at the enabling technologies and lessons learned.
The starting point is 5G New Radio, with the example of a project that is offering immersive music therapy based on virtual reality in a nursing home. Based on that, the current status of the technology will be presented. Some of the new features of 5G networks are already producing a substantial leap in the technology, enabling virtual and augmented reality (XR) services. They are subsequently explored and an architecture is proposed to provide such services today. Further, the possibility to provide such systems on the move is exemplified with the outcomes of a remote driving project. The future ahead includes new ways of interaction, adding the haptic dimension, and this will require new capabilities being offered from the mobile networks that need to be enabled in the evolution to 6G and beyond. A few of these enabling technologies are discussed in more detail, with focus on evolutions happening at the physical layer.
Biography
Ana García Armada is a Professor at Universidad Carlos III of Madrid, Spain, where she is leading the Communications Research Group. She has been a visiting scholar at Stanford University, Bell Labs and University of Southampton. She is an IEEE Fellow. She has published more than 300 papers in international journals and conference proceedings and she holds seven granted patents, all related to wireless communications. She is serving on the editorial board of IEEE Open Journal of the Communications Society (Associate Editor in Chief since 2024) and ITU Journal on Future and Evolving Technologies. She has been a member of the organizing committee of IEEE MeditCom 2024 (General Chair), IEEE WNCN 2024, IEEE Globecom 2022 and IEEE Globecom 2021 (General Chair), among others. She has received the Young Researchers Excellence Award from University Carlos III of Madrid. She was awarded the third place Bell Labs Prize 2014 for shaping the future of information and communications technology. She received the Outstanding service award from the IEEE ComSoc Signal Processing and Communications Electronics technical committee in 2019 and the Outstanding service award from the IEEE ComSoc Women in Communications Engineering Standing Committee in 2020. She received the IEEE ComSoc/KICS Exemplary Global Service Award in 2022.
Francisco Martínez Álvarez
Quantum Machine Learning for eHealth: Foundations, Opportunities, and Emerging Applications
Quantum Machine Learning is an emerging field at the intersection of quantum computing and artificial intelligence, with the potential to transform data intensive domains such as eHealth. This plenary talk will provide an accessible and broad introduction to the fundamental concepts of QML, including key quantum computing principles, hybrid quantum classical models, and current algorithmic approaches. The talk will then examine how these techniques may address inherent challenges to healthcare data, such as high dimensionality, complex feature interactions, and limited labeled datasets. Particular attention will be given to the current capabilities and limitations of near-term quantum devices, offering a realistic perspective on what can be achieved today. Finally, the session will explore promising application scenarios in eHealth, including medical imaging, biomarker discovery, personalized medicine, and clinical decision support systems. The goal is to provide attendees with both a conceptual foundation and a critical understanding of where QML can meaningfully contribute to healthcare innovation in the coming years.
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, awarded with the extraordinary Ph.D. prize.
He has completed up to five long research stays at top universities: Université de Lyon 1 (2008), New York University (2010 and 2024), Universidad de Chile (2012) and Université de Lyon 2 (2017).
His primary research areas include time series, quantum machine learning, explainable artificial intelligence, and big data analytics, with over 200 papers published. He has supervised nine doctoral theses and led many research projects, including five Spanish and two European ones. Along with Prof. Alicia Troncoso, he founded the Data Science & Big Data Lab in 2015.
The commitment to the knowledge transfer to industry is undeniable. Over the last few years, he has been the principal investigator in numerous IoT, machine learning, data mining, and artificial intelligence projects, collaborating with more than 10 firms.
From 2013, he has been holding management positions at Pablo de Olavide University, such as Secretary of the School of Engineering, Head of the Division of Computer Science, General Director for Infrastructures, Campus and Sustainability, and Secretary of the Doctoral Programme in Biotechnology, Engineering and Chemical Technology.
According to Stanford University’s analysis, he has been ranked as one of the world’s top 2% single-year impact scientists in the years 2022, 2023, 2024, and 2025, and career-long scientist impact in the years 2024 and 2025.
Aurel A. Lazar
Building the Foundations of Natural Olfactory Intelligence
The history of more than 70 years of AI research is still marked by Moravec’s paradox, whereby, tasks that are easy for humans to perform (e.g., sense of smell) are difficult for machines to replicate, whereas tasks that are difficult for humans (e.g., mathematical proofs) are relatively easy for machines to accomplish. Already in 1914, Alexander Graham Bell asked a group of students if they ever tried to measure a smell or measure the difference between one kind of smell and another. If you are ambitious to found a new science, he further argued, measure a smell. Progress in the last 110 years has been rather slow at building artificial olfactory systems.
In nature, fruit flies like many other insects and vertebrates, possess extraordinary abilities to distinguish and recognize different smells thanks to their hard-wired, innate brain circuits that evolved over millions of years. The operation of the early olfactory system of the fly appears to be largely conserved and it is recognized now that the same mechanisms must underlie the operation of brains of all sizes, including those of humans. Given their relatively small size (∼ 140,000 neurons), coupled with the behavioral richness that this brain supports, and the wide variety of techniques now available to study both brain and behavior, these advances present us with a unique opportunity to build from the ground up biologically informed intelligent machines/robots guided by olfactory information.
The ability to make the world of odorants intelligible is a key capability of the Drosophila olfactory system that we shall call olfactory intelligence. A key first element of olfactory intelligence in the Drosophila is the binding of odorant objects to receptors expressed in the olfactory sensory neurons. The early olfactory system of the fruit fly encodes the odorant object identity (semantic information) and the odorant concentration waveform (syntactic information) into a combinatorial neural code. Under this model, semantic information is time-independent and is characterized by a tensor, reflecting the fact that odorant object identity is encoded collectively with spatially-distributed sensors. Odorant syntax, on the other hand, is time-varying and embedded in the individual sensor responses.
Consequently, the two attributes of an odorant must be untangled by the olfactory system in order to recognize the identity of the odorant. This calls for i) explicitly modeling odorant stimuli in terms of their semantic and syntactic information content, and ii) exploring a new class of processors, called intelligent olfactory processors that process olfactory semantic information in the early olfactory system of the fruit fly (i.e., processors that extract, operate on and store semantic information flows), and, iii) defining a new representation of odorant semantics at the input of the associative learning compartment circuits that supports a high degree of semantic specificity (i.e., separation between different odorant identities).
To address these questions, we introduce a class temporal and spatio-temporal processing building blocks called differential Divisive Normalization Processors (DNPs) described by non-linear differential equations with largely stable temporal and spatio-temporal feedback loops. The feedback circuits in the early olfactory system discussed here are instrumental in extracting odorant identity information (semantics) from the confounding odorant syntactic information (concentration) as well as in boosting the accuracy in classifying odorant semantics. The differential DNP circuits provide a novel representation of the odorant semantics as a first spike sequence code. The code reflects the amplitude ranking that drives the early olfactory system in the time domain. The rank-based representation supports accurate classification of odorant semantics at negligible complexity. This is a rather simple methodology given that ranking in the amplitude domain is, by itself, NP-complete. This opens up a new research direction in feedback control/processing of semantic information in machine intelligence.
Biography
Aurel A. Lazar (Fellow’93, IEEE) was for 20 years leading a number of computer networking research groups in the Department of Electrical Engineering, at Columbia University in New York. He covered a broad set of research topics/fields, including building major switching hardware and architecting broadband kernels, pioneering the xbind open programmable network, and creating foundational game theory models for resource allocation. While revolutionary, the 1997 xbind platform faced severe real-world headwinds including performance bottlenecks and vendor resistance. His vision was vindicated in the late 2000s when the industry finally consolidated around centralized cloud computing. The industry realized that Lazar’s core tenet—-that networks must “innovate at the rate of the software development cycle rather than the sluggish rate of hardware”—-was correct. Modern architectures, from datacenter software-defined fabrics to current 5G Open RAN cloud controllers, are a direct realization of Lazar’s broadband kernel concept. Prior to receiving the 2003 IFIP/IEEE Dan Stokesberry Memorial Award he briefly ran a networking start-up as CEO.
He then switched his field of research to computational neuroscience in search of principles of building cognitive machines. His early work pioneered the loss free representation of visual fields and auditory scenes in the spike domain (time encoding machines).
His current research interests focus on the molecular architecture and functional logic of the brain of model organisms with a strong emphasis on the fruit fly brain. He leads research projects in Building Interactive Computing Tools for the Fruit Fly Brain Observatory, in Computing with Fruit Fly Brain Circuits and on Creating NeuroInformation Processing Machines. His research has drawn support from a number of funding agencies including the AFOSR, DARPA, NIH, and NSF.