جست‌وجوی استاد و پژوهشگر

Antonio Guimarãesمشاهده پروفایل

پژوهشگر

• Fully Homomorphic Encryption (FHE)
• Verifiable Computation
• Privacy-preserving technologies
• +۳ مورد دیگر

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.

• Chemometrics
• Information Theory
• Machine Learning
• +۹ مورد دیگر

Jordi Fonollosa Magrinya is an Associate Professor at the Barcelona School of Nautical Studies (FNB), Universitat Politècnica de Catalunya - BarcelonaTech (UPC), affiliated with the Department of Automatic Control and the Institut de Recerca i Innovació en Salut. He is a key member of the B2SLab - Bioinformatics and Biomedical Signals Laboratory and TecSalut - Grup de Recerca en Tecnologies de la Salut. Expertise: Chemometrics, Information Theory, Machine Learning, Sensors, Signal Processing Research Groups: B2SLab, TecSalut Teaching Center: Barcelona School of Nautical Studies (FNB) Email: jordi.fonollosa.m@upc.edu ORCID: 0000-0001-8854-8588 His research spans biomedical and environmental applications of sensor systems, with a focus on electronic noses, machine olfaction, and signal processing. He applies machine learning and chemometrics to develop intelligent sensing systems for health monitoring, environmental safety, and quality control. His work integrates interdisciplinary approaches from engineering, data science, and biomedicine to solve real-world problems in public health and sustainability. The recent articles highlight a strong trend in sensor technology, particularly gas sensor arrays for environmental and health monitoring. His publications cover applications in indoor air quality (CO2, ventilation), medical diagnostics (hypertension, Parkinsonism), food quality (olive oil), and global health (improved cookstoves). The keywords reflect a consistent focus on machine learning, signal processing, and sensor systems applied to biomedical and environmental challenges. 2024 Jordi Fonollosa Magrinya has supervised students, including J. Gelabert, and leads multiple competitive research projects funded by national and European programs. His grants focus on health technologies, environmental monitoring, and data-driven solutions for societal challenges. He actively collaborates with multidisciplinary teams across UPC and external institutions. He is a core member of the B2SLab - Bioinformatics and Biomedical Signals Laboratory and the TecSalut research group, both under the Institut de Recerca i Innovació en Salut. These teams focus on developing advanced sensor systems and data analysis methods for healthcare and environmental applications.

• Space Engineering
• Electrodynamic Tethers
• Airborne Wind Energy
• +۵ مورد دیگر

Gonzalo Sanchez Arriaga is a Full Professor in the Department of Aerospace Engineering at Charles III University of Madrid (UC3M), specializing in electrodynamic tethers for space applications and airborne wind energy systems. His research bridges theoretical plasma physics with practical engineering solutions for space debris mitigation and renewable energy generation. His primary research interests include Space Engineering, Electrodynamic Tethers, Airborne Wind Energy, Plasma Physics, Orbital Mechanics, and Renewable Energy. Dr. Sanchez Arriaga has pioneered techniques for propellant-less propulsion using electrodynamic tethers, developed advanced simulation tools like BETsMA v2.0, and conducted extensive experimental validation of airborne wind energy systems. His work demonstrates strong integration between theoretical modeling and practical implementation. Recent publications reveal a clear progression from fundamental plasma physics to practical engineering applications, with increasing emphasis on in-orbit validation and commercialization potential. His research shows particular innovation in bare-photovoltaic tethers, hollow cathode technology, and dynamic stall analysis for kite-based wind energy systems. Dr. Sanchez Arriaga has successfully secured multiple competitive research grants as principal investigator, including projects funded by the European Commission (E.T.PACK, E.T.COMPACT), European Space Agency, and Spanish government agencies (COMUNIDAD DE MADRID, AGENCIA ESTATAL DE INVESTIGACION). His research group has developed specialized software tools and secured patents related to orbital power generation systems. He has supervised numerous doctoral theses on tether technology and airborne wind energy systems, while maintaining active international collaborations with institutions including Freiburg University and TU Delft. His research group has produced over 50 publications in high-impact journals including Acta Astronautica, Journal of Propulsion and Power, and Wind Energy.

• Mobile Communications
• Mobile Network Security
• Machine Learning
• +۵ مورد دیگر

Abbas Jamalipour is a Professor of Ubiquitous Mobile Networking at the School of Electrical and Computer Engineering, University of Sydney, Australia. He holds prestigious fellowships from IEEE, IEICE, and IEAust. His research focuses on advanced communication technologies including mobile network security, machine learning applications in wireless systems, resilient disaster communications, IoT, and vehicular networks. Research Interests: Mobile Communications Architecture Cybersecurity for Mobile Networks AI-Driven Wireless Systems Disaster-Tolerant Communication Protocols Smart Transportation Networks IoT Ecosystems No specific grants, labs, or educational background details are provided in the text. His current position emphasizes cutting-edge research in next-generation networking solutions through interdisciplinary approaches combining artificial intelligence and telecommunications.

• Power Electronics
• Renewable Energy Systems
• Control Systems
• +۴ مورد دیگر

Daniel Santamargarita Mayor is a Researcher at the Department of Electronics, University of Alcalá. His research focuses on power electronics applied to renewable energy systems, particularly in the design and optimization of Dual Active Bridge (DAB) converters, thermal modeling using artificial neural networks, and control strategies such as H-infinity control. He also contributes to educational initiatives, including project-based learning in renewable energy courses and hardware-in-the-loop techniques for teaching power electronics. He holds a Doctorate from the University of Alcalá, where he completed his thesis titled Contribuciones al diseño de un 'Dual Active Bridge' usando técnicas de optimización in 2023, supervised by Dr. Emilio José Bueno Peña and Dr. Miroslav Vasic. His research interests span power electronics, renewable energy systems, control systems, thermal modeling, and educational methodologies in engineering. He specializes in optimizing power converter designs, such as DAB converters, and employs advanced techniques like artificial neural networks and deep reinforcement learning to enhance system efficiency and thermal management. Additionally, he integrates innovative approaches like hardware-in-the-loop simulations to improve practical training in renewable energy technologies. Recent publications highlight his work on thermal modeling for heat sinks, H-infinity control for DAB systems, and the integration of project-based learning in energy education. His research bridges theoretical advancements with practical educational tools, emphasizing both technical innovation and pedagogical excellence. No scientific awards have been listed for Daniel Santamargarita Mayor. Advising information is not provided in the text. No grants details are mentioned. He is a core member of the GEISER research group (Electronic Engineering Applied to Renewable Energy Systems), which focuses on advancing renewable energy technologies through applied electronic engineering solutions.

• Robotics
• Artificial Intelligence
• Sensor Systems
• +۴ مورد دیگر

Francisco Javier Acevedo Rodríguez is an Associate Professor at the University of Alcalá's Department of Signal and Communications Theory. Specializing in Robotics, Artificial Intelligence, and Sensor Systems , his research focuses on bioanalysis, multisensory integration, assistive technologies, and embedded AI systems . He leads the BAB_Group (Bioanalysis and Biosensors Group) and GRAM (Multisensorial Recognition and Analysis Group). He holds a PhD in Signal Processing from the University of Alcalá (2009), with a thesis on signal processing techniques for gas/liquid sensor systems. His work bridges theoretical signal processing with practical robotics applications, emphasizing real-world deployments in healthcare and navigation domains. Key technical contributions include semantic navigation systems, action recognition algorithms, and low-cost assistive robots for neurodevelopmental disorder patients. His research has addressed challenges in indoor localization, fall detection, and real-time video processing using ROS and AI-driven perception frameworks. He has published extensively in robotics, computer vision, and sensor systems since 2000. Notable work includes the SEMNAV navigation framework (2025) and a validated assistive robotics platform for daily living support (2021-2022).

• Signal Processing
• Microwave Engineering
• Image Classification
• +۴ مورد دیگر

Jesús Alpuente Hermosilla is an Associate Professor at the University of Alcalá, affiliated with the Department of Signal Theory and Communications and the Radiation and Sensing Group (RSG). His primary research focuses on signal processing, microwave engineering, and bioinspired systems. He holds a PhD in Engineering from the University of Alcalá (2014), with a thesis on automatic wood species classification using image analysis techniques. His research interests include non-destructive analysis of materials, fractal filtering for medical imaging enhancement, and antenna design for wireless communication systems. He has contributed to studies on microwave focusing effects in wood structures, long-term electromagnetic field exposure measurements, and bioinspired echolocation-based spectral techniques. His publications span topics from acoustic sensing algorithms to practical microwave engineering applications, reflecting a balance between theoretical innovation and applied engineering solutions. He has collaborated on projects involving automated production control systems and educational tools for electromagnetic network design. No specific scientific awards or grants are mentioned in the provided data. His advising role is not explicitly detailed, though his doctoral work demonstrates supervisory experience. He is actively involved in the Radiation and Sensing Group, contributing to interdisciplinary research at the intersection of signal theory and material characterization.

Javier Arroyo Gallardoمشاهده پروفایل

دانشیار

• Machine Learning
• Blockchain Technology
• Cryptocurrency Analysis
• +۵ مورد دیگر

Javier Arroyo Gallardo is an Associate Professor at the Department of Computer Science (Ciencias de la Computación) at Universidad de Alcalá (UAH), Spain. His research focuses on machine learning applications in finance, blockchain technologies, cryptocurrency analysis, and decentralized autonomous organizations (DAOs). He holds a PhD from Universidad Pontificia Comillas with a thesis on time series prediction methods. Research Interests: His work spans predictive modeling for interval/histogram time series, credit risk assessment using machine learning, blockchain governance structures, cryptocurrency market dynamics, and open collaboration platforms. Notable contributions include developing automated correction systems for programming practices (UnitJudge) and analyzing DAO ecosystems' power dynamics. Recent Trends in Publications: Recent work emphasizes interdisciplinary applications combining AI with financial systems, including P2P lending risk modeling, cryptocurrency portfolio optimization, and blockchain-based governance mechanisms. Over 50% of his 2020+ publications focus on blockchain-related topics. Awards & Recognition: No specific awards mentioned, though his work has been presented at high-profile events like the International Symposium on Open Collaboration. Grants & Advising: Advised multiple research projects on DAOs, crypto markets, and agent-based modeling. Active in collaborative frameworks with industry partners in financial technology and blockchain ecosystems. Labs & Teams: Associated with CSRG-UAH and the Cognitive Science Research Group, focusing on computational methods applied to complex systems analysis.

Augusto Luis Ballardiniمشاهده پروفایل

پژوهشگر

• Autonomous Vehicles
• Sensor Fusion
• Computer Vision
• +۳ مورد دیگر

Augusto Luis Ballardini is a Researcher at the Department of Automatic Control, Universidad de Alcalá (UAlcalá), Spain, within the INVETT Research Group focusing on Intelligent Vehicles and Traffic Technologies. He holds a PhD in Computer Science from the University of Milano-Bicocca (2017) and an MSc from the same institution (2012). His work integrates machine learning, computer vision, and sensor fusion for autonomous vehicle localization and decision-making systems. Key awards include a Marie Skłodowska-Curie Actions grant (GET-COFUND fellowship) and a Spanish Ministry grant under Maria Zambrano/NextGenerationEU. His research spans LiDAR-based localization, intersection classification, and uncertainty-aware neural networks. Education: PhD in Computer Science, University of Milano-Bicocca, 2017 MSc in Computer Science, University of Milano-Bicocca, 2012 Research Interests: Autonomous vehicles, sensor fusion, LiDAR and vision-based systems, machine learning for localization, traffic modeling, and safety-critical decision-making. His work emphasizes practical applications such as fail-aware odometry, knowledge graph-driven prediction, and urban scene understanding. Awards & Grants: Marie Skłodowska-Curie Actions research grant (2019) Maria Zambrano/NextGenerationEU project grant (2022) Lab & Collaborations: His research is conducted within the INVETT group at UAlcalá, focusing on interdisciplinary projects that bridge robotics, computer vision, and transportation engineering. Recent work includes benchmarking point cloud registration algorithms and developing explainable AI for autonomous driving.

• Machine Learning
• Health Informatics
• Artificial Intelligence
• +۳ مورد دیگر

José Luis Castillo Sequera is a Professor at the University of Alcalá, affiliated with the Department of Computer Sciences. His research focuses on machine learning applications in healthcare, environmental science, and telecommunications. He holds a PhD from the University of Alcalá (2010) with a thesis on evolutionary algorithms for document clustering in information systems. His research interests include health computing, intelligent systems, renewable energy forecasting, and data-driven solutions for societal challenges. Notable work includes developing predictive models for solar radiation, disease detection systems, and innovative health monitoring apps like 'Sedentary' for office workers. Recent publications emphasize machine learning applications in diverse domains: from optimizing agricultural yields and reducing water losses to predicting geological hazards using quantum computing. He explores blockchain for healthcare interoperability and VR-based language learning tools. His contributions span over 100 publications since 2010, addressing topics like smart lighting systems, IoT-based health monitoring, and educational technology. He leads the HCIS research group focusing on health computing and intelligent systems.

• Educational Technology
• Artificial Intelligence
• Remote Sensing
• +۴ مورد دیگر

Dr. Julia María Clemente Párraga is a Full Professor at the Universidad Autónoma de Madrid (UAM), affiliated with the Department of Automatic Control. She leads research in the GITA Teledetección Ambiental (Environmental Remote Sensing) group. Her academic background includes a Doctorate from the Universidad Politécnica de Madrid (2011), where her thesis focused on ontology-based student modeling and cognitive diagnosis, supervised by Dr. Angélica de Antonio Jiménez and Dr. Jaime Ramírez Rodríguez. Her research spans educational technology, artificial intelligence applications in learning systems, remote sensing for environmental monitoring, and ontology engineering. Notable contributions include adaptive recommender systems for competency-based learning, semantic web tools for student monitoring (e.g., STSIM), and visualization frameworks for post-fire ecosystem analysis (RPI Engine). She also explores virtual environments for space training and multilingual ontology development. Recent work (2023) leverages convolutional neural networks to analyze photoplethysmographic signals for stress detection. Prior efforts (2014–2018) emphasized student modeling through non-monotonic diagnosis and ontology networks. Early contributions (2005–2009) focused on integrity constraints in knowledge bases and SCORM-compliant course development. Her research bridges computer science and education, with applications in both academic and environmental domains. Collaborations include interdisciplinary projects on intelligent tutoring systems and geospatial data analysis.

• Computer Vision
• Deep Learning
• Autonomous Driving
• +۳ مورد دیگر

Héctor Corrales Sánchez is an academic at the Universidad de Alcalá , affiliated with the Department of Computer Science. His primary role is as a Labor Substitute Professor , focusing on research in Computer Vision, Deep Learning, and Autonomous Driving systems. He holds a Doctorate from the Universidad de Alcalá (2021) with a thesis titled Vehicle keypoint detection and fine-grained classification using deep learning , supervised by Dr. David Fernández Llorca and Dr. Ignacio Parra Alonso. His research interests emphasize intelligent transportation systems, traffic technologies, and machine learning applications in real-world scenarios. He is a member of the INVETT Group (Intelligent Vehicles and Traffic Technologies), which explores vehicle classification, localization, and autonomous driving challenges. His recent publications (2019–2022) highlight trends in CNN-based localization systems, vehicle pose estimation, and bias mitigation in classification models. Notable works include WiFiNet for indoor localization and studies on vehicle-to-vehicle communication performance. No scientific awards are explicitly mentioned, but his contributions to traffic-related AI suggest ongoing recognition potential. He has not listed any advised students in the provided data. His involvement with the INVETT Group underscores collaborative efforts in developing smart technologies for transportation infrastructure and autonomous systems.

• Ray-Tracing Techniques
• Airport Security Systems
• Wireless Localization Algorithms
• +۳ مورد دیگر

Antonio del Corte Valiente is an Associate Professor at the Department of Automática, University of Alcalá. His research focuses on enhancing airport safety and precision localization through ray-tracing techniques, wireless networks, and artificial neural networks. He earned his PhD in 2011 with a thesis on improving airport security via efficient localization algorithms using WLAN networks and ray-tracing methods, supervised by Dr. Óscar Gutiérrez Blanco and Dr. José Manuel Gómez Pulido. His work integrates interdisciplinary approaches, combining electrical engineering with aerospace and civil engineering challenges. Key contributions include developing hybrid localization systems (ray-tracing + fingerprinting) for indoor-outdoor environments, neural network models for energy-efficient lighting, and non-cooperative aircraft surveillance systems using short-range radar. He is affiliated with the HCIS group (Health Computing and Intelligent Systems), though his primary research remains in transportation and airport infrastructure. No scientific awards are listed, but his publications span over a decade of consistent output in localization, aviation safety, and smart systems. His advising record is not explicitly detailed here, but his doctoral work indicates mentorship potential in advanced localization and safety engineering. Ongoing research likely explores further integration of AI and sensor networks in critical infrastructure systems.

• Photovoltaic Materials
• Quantum Dot Systems
• Complex Networks
• +۵ مورد دیگر

Lucas Cuadra Rodríguez is a Full Professor in the Department of Signal Theory and Communications at Universidad Alfonso X El Sabio (UAX). He holds a PhD from Universidad Politécnica de Madrid (2004), specializing in intermediate band solar cell development. His research spans quantum dot systems, network science applications in materials and energy systems, and machine learning for environmental and engineering challenges. Key areas include photovoltaic materials, complex network modeling of disordered systems, and renewable energy optimization. Education: Doctorado en Física from Universidad Politécnica de Madrid (2004), supervised by Dr. Antonio Marti Vega and Dr. Antonio Luque López. His work bridges physics, computer science, and engineering, with a focus on interdisciplinary solutions for energy and environmental systems. Research Interests: His primary focus is on developing novel semiconductor solar cell structures, particularly quantum dot-based intermediate band solar cells, and applying network science to study carrier transport in nanomaterials. He also explores machine learning applications in Earth observation, wind energy prediction, and complex system analysis. His recent work emphasizes spatially embedded networks and their relevance to material science and renewable energy infrastructure. Notable Projects: Leading the GHEODE Research Group (Modern Heuristics and Network Design), he investigates optimization algorithms for smart grids and network robustness. Recent studies include modeling electron transport in van der Waals materials and analyzing fog event persistence through nonlinear dynamics. Awards: No specific awards mentioned in the provided text. Advising & Grants: While his PhD supervision details are noted, no current student advisees or grant projects are detailed here. His work often involves collaborative interdisciplinary projects with engineering and environmental science teams. Labs/Teams: Active in the GHEODE Group at UAX, focusing on heuristic algorithms and network-based solutions for engineering challenges.

• Mechanical Engineering
• Electrical Engineering
• Materials Science
• +۳ مورد دیگر

Efrén Díez Jiménez is a Professor at the Universidad de Alcalá , affiliated with the Department of Signal Theory and Communications. His research focuses on interdisciplinary engineering challenges at the intersection of mechanical systems, electromagnetism, and energy technologies. He is part of the TM Group (Mechanical, Thermal, and Electrical Engineering & Technologies), exploring innovations in magnetic systems, renewable energy, and biomedical applications. He holds a PhD from Universidad Carlos III de Madrid (2012) on Foundations of Engineering of Mechatronic Systems with Magnets and Superconductors , advised by Dr. José Luis Pérez Díaz. His work emphasizes practical applications such as geothermal cooling for photovoltaics, microfabrication of magnetic components, and wireless power transfer for medical devices. Key research areas include: Magnetic gearboxes and superconductor-based devices MEMS and micromotor design Energy-efficient systems for aerospace and biomedical use Model-based systems engineering for microdevices His recent publications (2023–2024) highlight advancements in miniaturized antennas, cryogenic magnetorquers, and geothermal-enhanced solar panels. He has contributed to educational initiatives integrating project-based learning in mechanical design. Labs/Teams: TM Group (Mechanical, Thermal, and Electrical Engineering & Technologies).