These are accepted Special Sessions so far. The list will be enlarged as more sessions are proposed.
Recall the procedure for Special Session Proposals.
| ID | TITLE | ORGANIZERS |
| SS01 | Current and Future Advances in Brain-Computer Interfaces | Ivan Volosyak Ricardo Ron Angevín Roberto Hornero |
| SS02 | ITOMAD – Intelligent Techniques for Optimization, Modeling, and Anomaly Detection | Esteban Jove Paulo Novais José Luis Calvo Rolle |
| SS03 | Healthcare Applications: Deep Learning, LLMs, and Graph Methods | Miri Weiss Cohen Anat Dahan Anna Ghidotti |
| SS04 | Geometry, Games and Trustworthy Neural Learning for Economic and Financial Decisions | Massimiliano Ferrara |
| SS05 | Frugal deep learning for efficient architectures and resource-aware learning | Absalom El-Shamir Ezugwu Tehnan Mohamed Vernon Vicus Kok Diego Oliva |
| SS06 | Deep Learning applied to Computer Vision, Healthcare and Robotics | Enrique Dominguez José García-Rodríguez Ramon Moreno Jiménez |
| SS07 | Neural and Foundation Models for Biomedical Signal and Data Analysis (NeMoBio) | Andrés Ortiz Diego Castillo-Barnes |
SS01: Current and Future Advances in Brain-Computer Interfaces
Ivan Volosyak
Rhine-Waal University of Applied Sciences, Germany
Ricardo Ron Angevín
University of Málaga, Spain
Roberto Hornero
University of Valladolid, Spain
Abstract
A Brain-Computer Interface (BCI) is based on the analysis of brain activity to provide a non-muscular channel for sending messages and commands to external devices. Initially developed to enable communication for individuals with severe neuromuscular disorders, including those with locked-in syndrome, BCI technology has rapidly evolved into a multidisciplinary research field with applications extending far beyond assistive communication.
Today, BCIs have the potential to transform the way humans interact with digital systems and their environment, supporting applications in communication, neurorehabilitation, human-computer interaction, virtual and augmented reality, neurofeedback, cognitive monitoring, and assistive robotics. Recent advances in signal acquisition technologies, machine learning, deep learning, artificial intelligence, multimodal integration, and adaptive paradigms are accelerating the development of more accurate, robust, and user-friendly BCI systems.
The aim of this special session is to bring together researchers working on the latest advances in Brain-Computer Interfaces, including novel signal processing methods, machine learning and artificial intelligence approaches, innovative paradigms, hybrid BCIs, user adaptation strategies, hardware developments, and emerging applications. The session welcomes both methodological contributions and application-oriented studies that advance the state of the art in BCI research.
The organizers have successfully hosted special sessions on BCI technologies at previous IWANN conferences: IWANN09 (Salamanca), IWANN11 (Málaga), IWANN13 (Tenerife), IWANN15 (Palma de Mallorca), IWANN17 (Cádiz), IWANN 19 (Gran Canaria), IWANN23 (Ponta Delgada), and IWANN25 (A Coruna). As such, the BCI special session has become a long-standing tradition within the IWANN conference series, providing an established forum where researchers from academia and industry meet every two years to present recent advances, exchange ideas, and discuss future directions in Brain-Computer Interface research.
Organizers
Dr. Ivan Volosyak received the Diploma in the field of automation and control of technical systems from the Dnepropetrovsk State University, Dnepropetrovsk, Ukraine, in 1998, and the Ph.D. degree in electrical engineering from the University of Bremen, Bremen, Germany, in 2005. He is currently a Professor for Biomedical Engineering at the Rhine-Waal University of Applied Sciences, Kleve, Germany. Previously, he was a Postdoctoral Research Fellow at the Institute of Automation, University of Bremen, and Project Manager of several national and the European Union projects carried out at the University of Bremen, Germany. From 2005 to 2008, he has held visiting positions at the Institute for Knowledge Discovery, Graz University of Technology, Austria, and at the Centre for Rehabilitation Engineering, Glasgow University, U.K. His research interests include brain-computer interfacing, signal processing, digital image processing, service robotics, and assistive technology with the primary focus on applications in spinal cord injury rehabilitation.
Dr. Ricardo Ron-Angevin received the Engineer of Telecommunication and Ph.D. degrees from the University de Malaga, Spain, in 1994 and 2005, respectively. In 1995, he joined the Escuela Tecnica Superior de Ingenieros de Telecomunicacion of Malaga, where he is a Full Professor with the Electronic Technology Department. He is a member of DIANA research group, Manager and Coordinator of the UMA-BCI group of the University of Málaga and Project Manager of several national projects. His research interests include the design of brain-computer interfaces and assistive technology.
Dr. Roberto Hornero is Full Professor in the Department of Signal Theory and Communications at University of Valladolid (Spain) and Director of the Biomedical Engineering Group at the University of Valladolid (GIB-UVa), whose research interests are connected with the field of Big Data in biomedical signals and medical images. He has opened different research lines in the field of biomedical signal processing: automatic processing of pulse oximetry and overnight polysomnography signals to help in the diagnosis of obstructive sleep apnea, EEG and MEG analysis to help in the diagnosis of neurodegenerative diseases, retinal image analysis to automatically detect lesions associated with Diabetic Retinopathy, and the development of Brain Computer Interface systems to improve the quality of life of disabled people. He is Fellow of the European Alliance of Medical and Biological Engineering and Science (EAMBES), Senior Member of IEEE Society for Engineering in Medicine and Biology (EMBS) and Vice-President of the Spanish Biomedical Engineering Society (SEIB).
SS02: ITOMAD – Intelligent Techniques for Optimization, Modeling, and Anomaly Detection
Esteban Jove
University of A Coruña, Spain
Paulo Novais
University of Minho, Portugal
José Luis Calvo Rolle
University of A Coruña, Spain
Abstract
Regardless of the context, it is essential to tackle critical challenges due to various factors such as climate change, an aging population, and new industrial demands. These challenges include minimizing energy consumption and emissions, enhancing human quality of life, and meeting industrial improvement needs. Those involved in industry and academia cannot overlook these realities, as they will shape the future.
In this context, the industry, along with areas such as building management and assistive technologies, plays a crucial role in developing various emerging techniques to achieve the previously mentioned goals. While traditional methods have met current demands, there is a clear need for new advanced improvements in the future.
This session offers an exciting opportunity to present and discuss the latest theoretical advancements and practical applications in Optimization, Modeling, and Anomaly Detection using computational intelligence methodologies. This includes, among other topics, the following areas of focus:
- Energy efficiency and optimization.
- Control Techniques efficiency and optimization.
- Traditional systems improvement.
- Industrial control new techniques.
- Modeling of complex systems.
- Process optimization new techniques.
- Fault Detection and Diagnosis.
- Techniques to improve robustness against system failure.
- Computational intelligence developments aimed at human beings.
Organizers
Dr. Esteban Jove is an assistant professor in Systems Engineering and Automatics at the Department of Industrial Engineering of the University of A Coruña (UDC). His main research lines were initially focused on hybrid intelligent systems to model non-linear systems using artificial intelligence techniques combined with clustering methods. This proposal is successfully applied to predict a wide range of industrial and biological systems, among others. Then, his research continued with a new research line dealing with anomaly detection using one-class techniques and projectionist methods in industrial processes and cybersecurity systems.
Dr. Paulo Novais is a Full Professor of Computer Science at the Department of Informatics, the School of Engineering, the University of Minho (Portugal) and a researcher at the ALGORITMI Centre in which he is the leader of the research group ISLab – Synthetic Intelligence lab, and the coordinator of the research line Computer Science and Technology (CST). He is the coordinator of LASI (Intelligent Systems Associate Laboratory), director of the PhD Program in Informatics , and co-founder and deputy director of the Master in Law and Informatics program at the University of Minho. He started his career developing scientific research in the field of Intelligent Systems/Artificial Intelligence (AI), namely in Knowledge Representation and Reasoning, Machine Learning and Multi-Agent Systems. In recent years, his interest was absorbed by the different, yet closely related, concepts of Ambient Intelligence/Ambient Assisted Living, Conflict Resolution, Behavioural Analysis, Intelligent Tutors and the incorporation of AI methods and techniques in these fields. His main research aim is to make systems a little smarter, intelligent and also reliable.
Dr. José Luis Calvo Rolle is a full professor in Systems Engineering and Automatics at the Department of Industrial Engineering of the University of A Coruña (UDC). He is a permanent researcher at the Center for Research in Information and Communication Technologies (CITIC) of the UDC. He is the coordinator of the research group Cybernetics Science and Technology, recognized as a Group with Growth Potential (GPC) by the Xunta de Galicia, and director of the Environmental Radioactivity Laboratory of the UDC. His main lines of research focus on applying intelligent techniques to different fields. In the last decade, he has worked, especially in industrial applications in the fields of biomedicine and energy. He has also developed applications based on soft computing for decision-making systems and machine learning techniques for pattern recognition and anomaly detection. Currently, his research is focused on complex system modeling, digital twin development, fault and anomaly detection, and cybersecurity, in most cases, with the goal of system optimization and control.
SS03: Healthcare Applications: Deep Learning, LLMs, and Graph Methods
Miri Weiss Cohen
Braude College of Engineering, Israel
Anat Dahan
Braude College of Engineering, Israel
Anna Ghidotti
University of Bergamo, Italy
Abstract
Deep learning, large language models (LLMs), and intelligent systems are increasingly used in healthcare and rehabilitation, both to support clinical decision-making and to represent and analyze medical data. The rapid growth of healthcare data, together with advances in methodology and computational capacity, has led to a range of approaches for automatic feature extraction, classification, segmentation, and predictive modeling. Graph-based methods and network representations extend these approaches to medical and neurophysiological data. Computational biomarkers and morphological analysis are applied to diagnosis and disease prevention. These methods are also used in human modeling, pose assessment, and the evaluation of patients before and after clinical procedures.
This special session aims to bring together researchers working on the development, validation, and application of these methods.
We invite contributions that present original methods, applications, and evaluation studies, including but not limited to the following topics:
- Deep learning methods for medical image classification and segmentation
- Large language models for clinical decision support
- Graph-based methods and network representations for medical and neurophysiological data
- Computational biomarkers for diagnosis
- Intelligent systems for pre- and post-procedural patient assessment
- AI-based morphological analysis for disease prevention and diagnosis
- Human modeling and pose assessment in rehabilitation
Organizers
Dr. Miri Weiss Cohen is a Professor in the Department of Software Engineering at Braude College of Engineering and head of the Computer Science program. She holds a D.Sc. from the Technion – Israel Institute of Technology. Her research applies machine learning and optimization methods to engineering and medical problems. In medical imaging, she develops deep learning models, including optimized convolutional neural networks (CNNs) and attention-based architectures, for the segmentation, classification, and 3D reconstruction of MRI and CT scans. Applications include detecting brain tumors and liver disease and analyzing orthopedic morphology. Through interdisciplinary collaborations, she also applies machine learning to rehabilitation technologies and human–computer interaction. In evolutionary optimization, she uses genetic algorithms and differential evolution to address problems in UAV path planning, electric vehicle routing, and industrial scheduling.
Dr. Anat Dahan is a Senior Lecturer and Head of the Department of Software Engineering & Information Systems at Braude College of Engineering. Her research focuses on computational neuroscience and human motor control with applications in healthcare and rehabilitation. Her work combines signal processing, graph theory, and network analysis for modeling complex brain and behavioral data. In computational neuroscience, she develops data-driven methods for analyzing EEG and functional near-infrared spectroscopy (fNIRS) signals, including hyperscanning data for investigating inter-brain synchrony and social interaction. Her research also explores AI-based approaches for rehabilitation and human–computer interaction, including behavioral and physiological sensing, virtual reality, and personalized rehabilitation.
Dr. Anna Ghidotti pursued a MS in Engineering and Management for Health at the University of Bergamo and a PhD in Technology, Innovation and Management at the University of Bergamo and University of Naples Federico II. Currently, she is assistant professor at the University of Bergamo. Her research focuses on biomedical engineering, with particular emphasis on collaborations with hospitals to address clinical needs through advanced engineering tools. Her work focuses on the use of deep learning for the automatic segmentation and morphological analysis of medical images. She also applies computer-aided design techniques to develop customized prostheses. In addition, she uses motion-capture systems and machine learning to support rehabilitation assessment, monitoring, and disease progression prediction.
SS04: Geometry, Games and Trustworthy Neural Learning for Economic and Financial Decisions
Massimiliano Ferrara
Università degli Studi Mediterranea di Reggio Calabria, Italy
Abstract
Neural networks are increasingly entrusted with decisions that move prices, allocate capital, teer public procurement and shape how organisations adopt generative AI. In these settings accuracy alone is not enough: models must respect the geometry of the feasible set they act upon, anticipate the strategic response of the agents they affect, and remain explainable to regulators and decision makers. This special session gathers contributions where Riemannian ptimisation, game theory and explainable neural learning meet economic and financial problems, offering IWANN a transversal, mathematically grounded track at the frontier between computational intelligence and the decision sciences.
Contributions are invited on, but not limited to, the following topics:
- Riemannian and geometric optimisation for training neural networks
- Geometric deep learning for financial time series and networks
- Constrained portfolio optimisation and asset pricing with neural methods
- Interpretable state-space and spectral features (e.g. Matrix Pencil) for forecasting
- Variational inequalities and equilibrium problems solved with deep learning
- Game-theoretic models of learning agents and AI adoption
- Generative AI in organisations: diffusion, perception gaps, governance
- Explainable AI for credit, market and supply chain risk
- Hybrid MCDA and neural frameworks for critical decisions
- Robustness, adversarial resilience and uncertainty quantification in economic models
- Quantum and kernel-based learning for finance
- Trustworthy and regulation-compliant AI in finance and public administration
Organizer
Dr. Massimiliano Ferrara is Full Professor of Mathematical Economics, Artificial Intelligence and Machine Learning at the Università degli Studi Mediterranea di Reggio Calabria, where he is Scientific Director of the Decisions LAB at the Department of Law, Economics and Human Sciences. He is affiliated with ICRIOS at Bocconi University (Milan) and with an advanced computing laboratory at Istanbul Okan University. His research lies at the crossroads of differential geometry and optimisation on Riemannian manifolds, multi-time geometric dynamics, game theory and Nash equilibria, pursuit-evasion differential games and variational methods, with applications to machine learning, finance and AI governance. He is the author of more than two hundred peer-reviewed papers, serves as Associate Editor of Annals of Operations Research and has acted as handling editor for Scientific Reports. He is the author of the monograph Intelligenza Artificiale Affidabile (EGEA, 2nd ed., 2026). He holds a PhD Honoris Causa in Computer Science from Georgian National University SEU (Tbilisi, 2026), is a Cavaliere della Repubblica Italiana, and is included in the Stanford/Elsevier World’s Top 2% Scientists ranking (2026 edition, single-year list).
SS05: Frugal deep learning for efficient architectures and resource-aware learning
Absalom El Shamir Ezugwu
North-West University, South Africa
Tehnan Mohamed
North-West University, South Africa
Vernon Vicus Kok
North-West University, South Africa
Diego Oliva
Universidad de Guadalajara, Mexico
Abstract
Deep learning has advanced computer vision, natural language processing, healthcare, forecasting, and intelligent decision-making, but its computational, memory, energy, and data requirements limit deployment in resource-constrained environments. Frugal deep learning addresses these challenges by developing efficient learning systems that balance predictive performance with reduced computational costs, energy consumption, memory use, and data demands throughout the learning pipeline.
This special session will bring together methodological, theoretical, and application-oriented research on resource-efficient deep learning, including lightweight architectures, efficient attention, model compression, knowledge distillation, neural architecture search, self-supervised learning, parameter-efficient fine-tuning, and hardware-aware optimisation. Contributions exploring trade-offs among accuracy, efficiency, robustness, and generalisation are particularly welcome.
The session will also highlight applications in healthcare, precision agriculture, environmental monitoring, industrial intelligence, and edge computing, welcoming novel methods and rigorous empirical studies for low-resource settings. It aims to advance accessible, scalable, and practically deployable deep learning models, aligning with IWANN’s interests in deep learning, learning and adaptation, computational intelligence, and real-world AI applications.
Organizer
Dr. Absalom El-Shamir Ezugwu earned his B.Sc. in mathematics with computer science, followed by M.Sc. and PhD degrees in computer science from Ahmadu Bello University, Zaria, Nigeria. He is a full Professor of Computer Science within the Unit for Data Science and Computing at North-West University, Potchefstroom, South Africa. Absalom has successfully mentored and graduated numerous PhD and MSc students in computer science, achieving a notable record of high-impact publications in respected journals and conferences. He is also a visiting research Professor with the School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa. His research focuses on artificial intelligence, machine learning, deep learning, evolutionary computation, swarm intelligence, and nature-inspired algorithm design, with a specific emphasis on computational intelligence and metaheuristic solutions for real-world global optimisation problems.
Dr. Tehnan Mohamed is currently a Postdoctoral Researcher at North-West University, South Africa. Her research focuses on the application of artificial intelligence and data science to address real-world challenges. Her research interests include machine learning, deep learning, predictive modelling, and data-driven approaches for health and environmental applications. She is also interested in developing hybrid deep learning models to improve prediction accuracy and support evidence-based decision-making. Her broader research experience includes applying machine learning and deep learning techniques to healthcare-related problems.
Vernon Vicus Kok is currently a PhD student at North-West University in South Africa. The focus of his research is the application of Reinforcement Learning (RL) to mapless navigation for mobile robots. He is particularly interested in Deep Learning (DL), Computer Vision (CV), and simulation environments. He has several years of experience working with game engines to simulate and conduct experiments in RL and building robots for navigation and identification of potholes. He also has experience working with evolutionary methods to automate hyperparameter tuning and reward design for navigational agents.
Dr. Diego Oliva received a B.S. degree in Electronics and Computer Engineering from the Industrial Technical Education Centre (CETI) of Guadalajara, Mexico, in 2007 and an M.Sc. degree in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico, in 2010. He obtained a PhD in Informatics in 2015 from the Universidad Complutense de Madrid. Currently, he is an Associate Professor at the University of Guadalajara in Mexico. He is a member of the Mexican National Research System (SNII), a Senior Member of the IEEE, and a member of the Mexican Academy of Computer Sciences (AMEXCOMP). He is an editor of multiple journals, including Swarm and Evolutionary Computation; he also actively participates in and organises special tracks in conferences such as Evostar, CEC, and WCCI. His research interests include evolutionary and swarm algorithms, hybridisation of evolutionary and swarm algorithms, and computational intelligence.
SS06: Deep Learning applied to Computer Vision, Healthcare and Robotics
Enrique Domínguez
University of Malaga, Spain
José García-Rodríguez
University of Alicante, Spain
Ramon Moreno Jiménez
Grupo Antolin Ingeniería, S.A.U.
Abstract
Over the last decades there has been an increasing interest in using machine learning and in the last few years, deep learning methods, combined with other vision techniques to create autonomous systems that solve vision problems in different fields. This special session is designed to serve researchers and developers to publish original, innovative and state-of-the art algorithms and architectures for real time applications in the areas of computer vision, image processing, biometrics, medical imaging, virtual and augmented reality, neural networks, intelligent interfaces and biomimetic object-vision recognition.
This special session provides a platform for academics, developers, and industry related researchers belonging to the vast communities of Neural Networks, Computational Intelligence, Machine Learning, Deep Learning, Biometrics, Vision systems, Healthcare and Robotics, to discuss, share experience and explore traditional and new areas of the computer vision, machine and deep learning combined to solve a range of problems. The objective of the workshop is to integrate the growing international community of researchers working on the application of Machine Learning and Deep Learning methods in Vision, Health and Robotics to a fruitful discussion on the evolution and the benefits of this technology to the society.
The methods and tools applied to vision and robotics include, but are not limited to, the following:
- Computational Intelligence methods
- Machine Learning methods
- Self-adaptation and self-organisation
- Robust computer vision algorithms (operation under variable conditions, object tracking, behaviour analysis and learning, scene segmentation,,,,)
- Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
- Convolutional Neural Networks CNN
- Recurrent Neural Networks RNN
- Deep Reinforcement Learning DRL
- Hardware implementation and algorithms acceleration (GPUs, FPGA,s,…)
The fields of application can be identified, but are not limited to, the following:
- Video and Image Processing
- Video tracking
- 3D Scene reconstruction
- 3D Tracking in Virtual Reality Environments
- 3D Volume visualization
- Intelligent Interfaces (User-friendly Man Machine Interface)
- Multi-camera and RGB-D camera systems
- Multi-modal Human Pose Recovery and Behavior Analysis
- Gesture and posture analysis and recognition
- Medical imaging
- Biometric Identification and Recognition
- Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
- Surveillance systems
- Autonomous and Social Robots
- Robotic vision
- Industry 4.0
- IoT and Cyber-physical Systems
Organizers
Dr. Enrique Domínguez is a (full) professor in the Department of Computer Science from University of Málaga. He received his Ph.D. degree in Artificial Intelligence from University of Málaga (Spain). He has collaborated with several companies (Airzone, Fujitsu, Altra Corporacion, Fundación Andaluza de la Seguridad Social, Evita, Acerca, …) leading the computer vision workgroup of different research projects. Dominguez is author of over 50 publications, reviewer of several journals such as IEEE Trans. of Neural Networks and Learning Systems, Neurocomputing, Neural Networks, International Journal of Parallel, Emergent and Distributed Systems, Neural Computing & Applications, Optimization, etc. and an associate editor of the International Journal of Computer Vision and Image Processing (IJCVIP). In addition, he has participated chairing several special sessions or as traditional member of the program committee of several conferences such as WCCI, IJCNN, IWANN, BMIC, ICANN, ASC, EURO and others.
Dr. Jose Garcia-Rodriguez received his Ph.D. degree, with specialization in Computer Vision and Neural Networks, from the University of Alicante (Spain). He is currently Full Professor at the Department of Computer Technology of the University of Alicante. His research areas of interest include: computer vision, computational intelligence, machine learning, deep learning, pattern recognition, robotics, man-machine interfaces, ambient intelligence, computational chemistry, and parallel and multicore architectures. He has authored +150 publications in journals and top conferences and revised papers for several journals like Journal of Machine Learning Research, Computational intelligence, Neurocomputing, Neural Networks, Applied Softcomputing, Image Vision and Computing, Journal of Computer Mathematics, IET on Image Processing, SPIE Optical Engineering and many others, chairing sessions in many editions of IJCNN and IWANN and participating in program committees of several conferences including IJCNN, ICRA, ICANN, IWANN, KES, ICDP and many others.
Dr. Ramon Moreno is Engineer in Computer Science and holds a Ph.D. from the University of the Basque Country in the Computer Science and Artificial Intelligence program (2012). He has wide research experience in both national and international projects. He has worked in international research centers such as: INPE (Brazilian Space Agency), ISSSE (Robotics Group, University Paris 12), and national universities such as at UPV-EHU, CVC (Computer Vision Center – UAB), as well as technology centers: Vicomtech-BRTA, Lortek-BRTA. He has authored +50 publications in journals and top conferences and revised papers for several journals. Since 2021 he leads Big Data in the Advance Manufacturing 4.0 department in Antolin, addressing developments in Data Science, Artificial Intelligence, Computer Vision and Robotics.
SS07: Neural and Foundation Models for Biomedical Signal and Data Analysis (NeMoBio)
Andrés Ortiz
University of Malaga, Spain
Diego Castillo-Barnes
University of Alicante, Spain
Abstract
The increasing availability of heterogeneous biomedical data, together with recent advances in neural network architectures and foundation models, is creating new opportunities for intelligent systems in biomedical research and healthcare. These developments encompass a broad spectrum of data modalities, including physiological time series, medical imaging, multi-omics data, and multimodal clinical information. Neural approaches are enabling increasingly sophisticated methods for signal processing, representation learning, feature extraction, pattern recognition, and predictive modeling across diverse biomedical domains. In particular, the growing use of large language models and generative foundation models in clinical and biomedical contexts raises open questions regarding their reliability, including phenomena such as hallucinated outputs and cognitive biases that may influence human-AI decision-making in healthcare settings.
Despite these advances, biomedical data present substantial methodological challenges, including noise, non-stationarity, inter-patient variability, high dimensionality, limited annotated datasets, heterogeneous acquisition protocols, and the need for robust, generalizable, and interpretable computational models. Addressing these challenges requires continued progress in neural network architectures, foundation models, self-supervised learning, transfer learning, and multimodal data integration.
This Special Session aims to bring together researchers developing and applying neural network-based methodologies for biomedical signal and data analysis. It will provide a forum for presenting novel computational approaches and methodological advances involving physiological signals (e.g., EEG, ECG, CGM), medical imaging (e.g., MRI, PET, SPECT, CT), multi-omics data (e.g., genomics, transcriptomics, proteomics, metabolomics), and multimodal biomedical datasets.
The session particularly encourages contributions addressing applications in neuroscience, neurology, cardiology, endocrinology, oncology, and other biomedical fields, while maintaining a strong emphasis on methodological innovation in neural computation. The main objective is to encourage interdisciplinary collaboration between researchers in neural networks, biomedical signal processing, medical imaging, computational biology, and artificial intelligence in healthcare, promoting the development of advanced learning methodologies capable of addressing complex biomedical challenges.
Organizers
Dr. Andrés Ortiz received the B.Sc. degree in Physics (1998), the M.Sc. degree in electronics (2000) and the Ph.D. degree in 2008, all from the University of Granada. He then earned the Ph.D. degree from the University of Cádiz, Spain (2012). From 2000 to 2005 he was working as Networks and Systems Engineer with Telefonica, Madrid, Spain, where his work areas were high performance computing and network performance analysis and optimization. Currently, he is Full Professor at the Department of Communications Engineering of the University of Malaga and head of the research group on biomedical signal processing, computational intelligence and communications security (BioSiP, TIC-251). His research interests include statistical signal processing with biomedical applications, intelligence systems, computational neuroscience, and high performance computing. He has coauthored more than 200 technical journal and conference papers in these research areas, that have been cited more than 6300 times (h=40). He has also been principal investigator in different funded research projects related to biomedical signal processing and intelligent diagnostic systems, and has supervised 9 doctoral theses. He has served as editor and reviewer for several international journals and conferences.
Dr. Diego Castillo-Barnes received his Ph.D. in Information and Communication Technologies from the University of Granada, Spain, in 2021. He is currently a postdoctoral researcher at IBIMA Plataforma BIONAND and a Ramón y Cajal Research Fellow at the University of Málaga. His research focuses on biomedical signal processing, machine learning, and artificial intelligence, with particular emphasis on neural network architectures, advanced signal analysis, and computational modeling of biomedical data. His work spans neuroimaging, electrophysiological signals, CGM, and multimodal biomedical data, with applications in neuroscience, cardiometabolic diseases, and oncology.