Jesús Ureña Ureña is Full Professor of Electronics at the University of Alcalá, heading the GEINTRA research group in electronic engineering applied to intelligent spaces. His research develops positioning systems using ultrasonic/infrared technologies with applications in assisted living. Research focuses on: Infrared/ultrasonic positioning architectures Sensor fusion for 3D localization FPGA-based signal processing Industrial IoT and edge computing Non-intrusive load monitoring Developed systems include LOCATE-US ultrasonic positioning and QUAPOS visible light positioning. Leads projects on high-frequency signal acquisition and adaptive federated learning. Awarded UAH Social Council Award for 'Ambient intelligence for independent living' (2019).
Antonio Rubio Solá is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the High Performance Integrated Circuits and Systems Design (HIPICS) group. He holds an M.S. in Industrial Engineering (1977) and a Ph.D. in Electronic Engineering (1982), both from UPC. His research focuses on semiconductor technology evolution, integrated circuit design, and memristor-based neuromorphic systems. Key interests include nanoelectronics, energy-efficient computing, and biomimetic circuits. Rubio has contributed to advancements in memristive logic, graphene nanoribbon devices, and fault-tolerant circuit architectures. His work bridges theoretical research and practical implementation, with notable contributions in neuromorphic hardware, in-memory computing, and radiation-hardened electronics. Recent publications emphasize memristor applications in biological systems emulation, stochastic resonance phenomena, and energy-efficient data processing. Rubio actively participates in Spain’s neuromorphic technology initiatives and promotes sustainable microelectronics education through digital tools. Publications are accessible via UPC FenixDoc and the HIPICS e-prints repository. His research is driven by interdisciplinary collaboration, addressing challenges in next-generation computing paradigms and emerging technologies.
Antonio Guimarães is a postdoctoral researcher at the IMDEA Software Institute in Madrid, Spain. His research focuses on practical aspects of Fully Homomorphic Encryption (FHE), including verifiable FHE, fast bootstrapping algorithms, and efficient homomorphic evaluation of cryptographic primitives. He holds a Ph.D. in Computer Science from the University of Campinas (2019-2023), where he also completed his MSc (2017-2019) and Computer Engineering degree (2012-2016). During his Ph.D., he was a visiting student at Aarhus University (2022-2023). His work emphasizes privacy-preserving technologies and verifiable computation, with applications in secure cloud computing and encrypted machine learning. Education: Ph.D. in Computer Science, University of Campinas (2019-2023) Visiting Ph.D. Student, Aarhus University (2022-2023) MSc in Computer Science, University of Campinas (2017-2019) Computer Engineering, University of Campinas (2012-2016) His research interests include advancing FHE efficiency, developing verifiable computation frameworks for encrypted data, and exploring practical implementations of post-quantum cryptographic algorithms. He has presented work at venues such as CRYPTO, ASIACRYPT, and CHES, and contributed to open-source cryptographic libraries like MOSFHET and HELIOPOLIS. His recent projects focus on homomorphic evaluation of neural networks and optimizing FHE for real-world applications. His publications highlight contributions to bootstrapping algorithms, verifiable computation over approximate arithmetic, and privacy-preserving machine learning. These works emphasize balancing cryptographic security with computational efficiency. Grants and Collaborations: Collaborations include projects on secure transciphering for MPC and high-performance seismic data processing in cloud environments. His work often bridges theoretical cryptography with practical software implementations. Labs/Teams: His research is conducted within the IMDEA Software Institute’s cryptography group, focusing on applied cryptography and privacy-preserving technologies.
Juan Ignacio García Tejedor is a Professor at the Universidad de Alcalá , affiliated with the Department of Automática . He is a core member of the SRG-UAH Space Research Group , focusing on cosmic ray detection, space instrumentation, and embedded systems. He earned his Ph.D. from Universidad de Alcalá with a thesis on reconfigurable platforms for particle detectors (2021), supervised by Dr. Juan José Blanco Ávalos and Dr. Sebastián Sánchez Prieto. His research interests span cosmic ray physics , neutron monitoring , hardware virtualization for space systems , and neural networks in particle analysis . Notable projects include the ORCA Antarctic Cosmic Ray Observatory , the MITO muon telescope , and the ICaRO detector . He also contributes to satellite software development, including memory management and real-time systems. His recent work emphasizes adaptive neural network topologies for improving data analysis in particle detectors and tailored virtualization solutions for LEON processors in space missions. Collaborations include international efforts in Antarctic and high-altitude observatories. His publications reflect expertise in detector design, cosmic ray dynamics, and embedded space computing. He actively contributes to teaching modules on space data management and interdisciplinary education in physics and instrumentation.
Abdelhamid Tayebi Tayebi is an Associate Professor in the Department of Computer Science at the University of Alcalá, Spain. His research focuses on computer science, artificial intelligence, antenna design, numerical simulation techniques, and educational technology. He leads the Climate Physics Group (CPG) and the Group of Advanced Numerical Techniques (GTNA), specializing in interdisciplinary projects combining computational methods with real-world applications. He earned his Ph.D. in 2011 with a thesis on antenna design methodologies using the method of moments, supervised by Dr. Manuel Felipe Cátedra Pérez and Dr. Iván González Diego. His work bridges theoretical research with practical tool development, including web-based simulators for antenna optimization and gamified educational platforms. Key research areas include 3D reconstruction using OpenStreetMap and LiDAR, medical imaging classification challenges, and wireless network propagation modeling. He actively explores gamification strategies to improve student engagement in STEM education, particularly in programming courses. His publications span disciplines from antenna engineering to AI-driven healthcare solutions, demonstrating a commitment to both technical innovation and pedagogical excellence. He collaborates on projects such as the EDISON initiative for data analytics and contributes to open-source tools like the NewFasant Suite.
JUAN FCO GUERRERO MARTINEZ is a Professor in the Department of Electronic Engineering at the Universitat de València, School of Engineering. He is affiliated with the Group for Digital Design and Processing (GPDD), where he conducts research at the intersection of biomedical engineering, signal processing, and hardware systems. His work integrates real-time embedded systems with clinical applications in cardiology and neuroscience. His research interests include biomedical signal processing , cardiac electrophysiology , machine learning for healthcare , FPGA-based real-time systems , and neuromorphic computing . He has extensively studied ventricular fibrillation, EEG/ECG analysis, and neural signal classification, often applying advanced computational techniques to improve clinical diagnostics and interventions such as deep brain stimulation. The 15 most recent publications reflect a strong trend toward real-time, hardware-accelerated biomedical systems , particularly using FPGAs for neural networks and signal classification. His work bridges theoretical signal processing with practical implementations in medical devices, emphasizing efficiency, accuracy, and clinical applicability. Themes include the use of time-frequency analysis, KNN classifiers, and spiking neural networks for detecting arrhythmias and brain activity patterns. He has supervised research theses and contributed to educational tools in signal processing and biomedical engineering. His academic leadership is evident in curriculum development and educational software, such as MATLAB-based tools for data analysis. While no formal awards are listed, his sustained publication record and leadership in research groups underscore his scholarly impact. Guerrero Martinez leads or contributes to research on embedded systems for medical diagnostics , neural engineering applications , and educational innovations in engineering . His lab, associated with the GPDD group, focuses on co-designing hardware and software for adaptive biomedical systems, supporting both research and teaching in electronic and biomedical engineering.
Javier Montoyo Bojo is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante. He earned his Computer Science degree (1995) and PhD in Computer Science (2015) from the same institution. A member of the Robotics and Three-Dimensional Vision (RoViT) research group, he held the role of Deputy Director of Business Internships at the Higher Polytechnic School (2015-2021). His research spans Artificial Intelligence , 3D Vision , Image Processing , and Educational Technology . Key projects include GPU-accelerated RGB-D camera registration , 3D hand pose estimation , and assistive robotics for acquired brain injury patients. Over the last decade, his work has focused on educational reform under the ECTS system, particularly for Statistics and Software Development courses. Recent publications highlight advancements in gaze tracking (2023) and collaborative educational frameworks for sustainable water technology programs (2019). He has participated in 9 public research projects (2018-2023) as coordinator or collaborator, including initiatives in 3D object recognition and VR/AR assistant systems for autism/Phobias (2023-2026). Teaching contributions include 13 iterations of Hypermedia Programming II and 10 of Statistics for Computer Engineering. He has directed/co-directed 14 undergraduate/master's theses in the last 5 years. Former roles include academic coordination of business internships (2017-2021) and editorial contributions to Statistics education materials.
Denisa Constantinescu is a postdoctoral researcher at the Embedded Systems Laboratory (ESL) at École Polytechnique Fédérale de Lausanne (EPFL) since 2022, affiliated with EcoCloud for sustainable computing technologies. She holds a PhD in Mechatronics (2022) and a Master's in Computer Engineering (2017) from Universidad de Málaga, and a B.Sc. in Systems Engineering from University Politehnica of Bucharest (2015). Her research focuses on sustainable and energy-efficient algorithms for wearables, IoT, and data centers, with specialization in scientific computing for astronomy, mobile robot navigation, and biomedical domains . She has received recognition including the Intel oneAPI Innovator Award (2020) and the SCIE-ZONTA Award (2021) . Her recent publications address themes like FPGA acceleration in genomics and astronomy , privacy-preserving biomedical algorithms , urban digital twins for climate action , and energy-efficient interferometry for radio astronomy . She actively contributes to scientific community service as a reviewer and organizer for conferences like PASC25 and ICPP 2025. Intel oneAPI Innovator (2020) SCIE-ZONTA 2021 Award IMFAHE Shark Tank Contest Winner Erasmus Student Scholarship As a daily supervisor of 5 PhD students and 2 Master's theses at EPFL, and through her involvement with Campus Tech Chicas in Spain, she advocates for equal opportunities in education.
Arturo Morgado Estevez is a Professor at the University of Cadiz, working in the Department of Automation, Electronics, Architecture and Computer Networks Engineering. His primary affiliation is with the Engineering school at the University of Cadiz, where he leads research in the TEP940 Applied Robotics research group. His work spans multiple technical domains with a strong focus on neuromorphic engineering and robotics applications. His research interests encompass Neuromorphic Engineering, Robotics, Computer Architecture, Real-Time Computing, FPGA Design, Bio-inspired Computing, Address-Event-Representation Systems, and Embedded Systems. Morgado Estevez specializes in developing spike-based processing systems that mimic biological neural networks, particularly focusing on applications in robotics, computer vision, and sensor systems. His work bridges the gap between biological inspiration and practical engineering implementations, with particular emphasis on real-time performance and hardware efficiency. An analysis of his recent publications reveals a strong trend toward applied robotics and embedded systems, with increasing focus on medical applications, assistive technologies, and energy efficiency. His research has evolved from fundamental neuromorphic architectures to practical implementations in prosthetics, industrial inspection, and environmental monitoring systems. Many of his recent works combine machine learning techniques with specialized hardware implementations for specific application domains. Morgado Estevez has been actively involved in educational initiatives, particularly in computer science education and robotics teaching methodologies. His work includes developing innovative teaching approaches for programming languages and engineering education, with several publications focused on educational technology and pedagogical methods. His laboratory work centers around the TEP940 Applied Robotics research group, where he has developed multiple FPGA-based implementations of neuromorphic systems. His team has created complete spike-based architectures from Dynamic Vision Sensors to robotic motor control, demonstrating practical applications of bio-inspired computing in real-world robotic systems. The research group has made significant contributions to Address-Event-Representation processing and its implementation on parallel computing platforms.
Dr. José Antonio Pérez Carrasco is a Full Professor in the Department of Signal Theory and Communications at the University of Seville. He leads the Signal Processing and Communications research group and has participated in numerous biomedical engineering projects focused on medical imaging, surgical planning, and neuromorphic vision systems. Professional Status: Full Professor Research Group: Signal Processing and Communications His research integrates computer vision, machine learning, and biomedical applications to solve medical challenges such as skin lesion segmentation, thermographic diagnosis of hemangiomas, and 3D CT image analysis. He has developed innovative algorithms for bone/muscle segmentation and retroperitoneal tumor delineation, often using convex relaxation techniques and event-based processing. Dr. Pérez Carrasco has secured multiple research grants including Burn Image Analysis System (PI-1597/29/2016), Mitochondrial segmentation in CT images (PI-1209/2013), and MISSION ALPHA (AEI-010500-2023-46). He has directed the doctoral thesis of Cristina Suárez Mejías on tissue segmentation and mentored numerous projects in intelligent infrared vision sensors. He actively contributes to university teaching innovation, implementing participatory techniques in engineering education and integrating company tasks into practical subjects. His work bridges academia with industry through contracts like the System for bone marrow radiotherapy toxicity analysis (PI-2566/33/2025).
Monica Abella Garcia is an Associate Professor in the Bioengineering Department at University Carlos III of Madrid . She leads the Biomedical Imaging and Instrumentation Group , focusing on advanced imaging techniques and software development for preclinical and clinical applications. Subjects: Biology and Biomedicine , Computer Science , Medical Imaging , Materials Science , Physics Research Interests Her work spans CT and X-ray imaging , deep learning applications , beam hardening and scatter correction , GPU-accelerated algorithms , and biomaterials for bone regeneration . She develops tools like XAP-Lab , FUX-Sim , and BoneAnalytics for quantitative imaging and protocol design. Projects & Grants Principal investigator on grants from Instituto de Salud Carlos III , Agencia Estatal de Investigación , and Banco Santander , focusing on AI in radiology , low-dose CT systems , and Covid-19 diagnostics . Co-investigator in EU-funded initiatives like ASPIDE and INFIERI , and MIT collaborations under the M+PET Project . Patents & Software Holds patents for tomography generation methods and developed software tools: 3DSTITCH (volume stitching), RadBoost (image enhancement), REXCT (fast reconstruction).
María Dolores Pérez Godoy is a full-time Professor in the Department of Computer Science at the University of Jaén. She contributes to the Andalusian Interuniversity Institute for Data Science and Computational Intelligence and leads the Intelligent Systems and Data Mining research group. PhD in Computer Science (2010) - University of Jaén Thesis: Hybrid cooperative-competitive evolutionary methods for radial basis function networks Research Focus: Computational Intelligence, Time Series Forecasting, Data Mining, Evolutionary Algorithms, Neural Networks, and Big Data Applications. Her work bridges theoretical advancements in RBFN design with practical implementations in agriculture (olive oil price forecasting) and resource-constrained systems. Recent Article Trends: 2025 work addresses multilabel imbalance with diffusion models. 2024 contributions include tools like Nets4Learning platform, DESReg library, and data governance frameworks. Earlier studies explore transformer models, clustering for crop mapping, and data stream classification. Technical Contributions: Developer of GRNN multi-series forecasting, data stream neural network implementations, and MEFASD-BD multi-objective evolutionary algorithms.
Nathan Drucker is a Postdoctoral Researcher at IBM Research Europe-Zurich's Science of Quantum and Information Technology group, focusing on quantum materials with extreme transport properties for next-generation devices. Prior to IBM, he worked as a graduate research assistant at MIT and held a Department of Energy Fellowship at the Stanford Linear Accelerator. Ph.D. and M.S. in Applied Physics from Harvard University Secondary field in Science, Technology & Society B.S. in Materials Science & Engineering and Physics from Carnegie Mellon University His research combines experimental techniques like X-ray/neutron scattering with machine learning to explore non-Ohmic transport phenomena in topological semimetals. He also contributed to semiconductor policy analysis at Harvard Kennedy School's Belfer Center under Ash Carter. Quantum materials characterization Mesoscale/nanoscale transport engineering High-throughput X-ray scattering data analysis Science policy development Scientific awards include a Department of Energy Fellowship. His work spans condensed matter physics, materials discovery, and semiconductor innovation.
Joaquín Ortega Castro is a Senior Lecturer (Associate Professor level) in the Department of Chemistry at the University of the Balearic Islands (UIB) . He earned his PhD in Chemical Sciences from the University of Granada in 2007 and carried out post-doctoral research stays at the University of Erlangen-Nürnberg and University of Cambridge . Educational background PhD in Chemical Sciences, University of Granada (2007) Research stays: University of Erlangen-Nürnberg, University of Cambridge (×2) Research interests Dr Ortega Castro’s work lies at the intersection of theoretical chemistry, computational chemistry, and materials science . His research spans: Environmental chemistry: selective detection and removal of heavy metals, organic contaminants in membranes Solid-state chemistry: CO₂ capture on porous frameworks, halide perovskites for photovoltaics Photo-switchable materials: design of molecules for LEDs, luminescent probes, and optoelectronic devices Surface science: adsorption of organics on mineral surfaces Pharmaceutical chemistry: crystallization inhibitors and public–private collaborations Across these domains he applies quantum-chemical methods, molecular dynamics, multiscale modeling, and machine-learning techniques to predict structure–property relationships and guide experiments. Scientific output and recognition With 98 peer-reviewed articles and participation in 13 funded projects , his work is internationally recognized. He has been awarded two consecutive six-year research accreditation periods and four six-year teaching accreditation periods by the Spanish national evaluation agency (ANECA/CNEAI). Advising and coordination roles Directed 3 doctoral theses Supervised 4 master’s theses (TFM) Supervised 5 bachelor’s theses (TFG) Local coordinator of the Erasmus Mundus Master’s Degree in Theoretical Chemistry and Computational Modelling (TCCM) Laboratory and team affiliations He is an active member of the Flow Injection and Trace Analysis (FI-TRACE) and Nanocapture and Supramolecular Systems (SUPRANANO) consolidated research groups. Additionally, he manages the social media outreach for the UIB Department of Chemistry on Twitter (@DQuimicaUIB) and Instagram (rsociales_dqu), promoting scientific dissemination and community engagement.
Dr. José SantaMaría López is a Professor at the Department of Computer Science , University of Jaén, specializing in Machine Learning , Medical Imaging , and Evolutionary Computation . His work bridges Artificial Intelligence with practical applications in Healthcare and Security . Current research focuses on Trustworthy AI , Explainable AI , and 3D Modeling for forensic identification. His Google Scholar profile highlights publications on adversarial attacks, data fusion, and real-time diagnostics using Deep Learning . He employs Evolutionary Algorithms and Fuzzy Logic for optimization in medical imaging and vehicular networks. Key trends in his recent publications include Decentralized Learning , Security in AI , and Parallel Computing for healthcare applications.
