Find Academic Supervisors

• Passive Radar Systems
• Signal Processing
• Sensor Networks
• +5 more

Nerea del Rey Maestre is an Assistant Professor in the Department of Signal Theory and Communications at the Universidad de Alcalá (UAH), Spain. She holds a PhD in Engineering from UAH (2018), focusing on Design and optimization of robust detection techniques in passive radar applications , supervised by Dr. María del Pilar Jarabo Amores and Dr. Manuel Rosa Zurera. Her research centers on intelligent passive radar systems, emphasizing clutter modeling, target detection, and surveillance applications. She leads the AES3 research group (Acoustic and Electromagnetic Smart Sensor Networks and Signal Processing), developing technologies for drone detection, ground traffic monitoring, and maritime surveillance using DVB-T/S, GPS, and SAR-based passive radar architectures. Her work spans signal processing innovations such as neural network-based CFAR detectors, planar antenna arrays, and decentralized radar processing frameworks. Key focus areas include non-coherent spatial integration, motion compensation for high-speed targets, and robust detection in non-homogeneous environments. Experimental validations include real-world scenarios like urban traffic imaging and coastal monitoring with TerraSAR-X and Sentinel-1 data. Her contributions address challenges in radar clutter characterization, Doppler shift compensation, and passive array optimization using commercial off-the-shelf (COTS) antennas. Ongoing projects involve the IDEPAR demonstrator for terrestrial radar surveillance and machine learning techniques for small boat detection in SAR clutter. Research funding has supported projects like the IDEPAR technological demonstrator and studies on PAZ satellite-based passive radar applications. Collaborations include multi-disciplinary teams tackling sensor network design, signal processing algorithms, and real-world deployment challenges in surveillance systems.

• Blockchain Technology
• Data Analytics
• Cybersecurity
• +7 more

Salvador Sánchez Alonso is a Professor at the Department of Computer Languages and Systems, Universidad de Alcalá. He holds a PhD from Universidad Politécnica de Madrid (2005) under Dr. Miguel Ángel Sicilia Urbán. His research focuses on blockchain technology, cybersecurity, linked data, healthcare informatics, and educational technology. He actively contributes to the I4 research group (Intelligence, Innovation, Internet, and Information), exploring topics like privacy-preserving systems, social network analysis, and open data principles. His work bridges theoretical advancements with practical applications, such as predictive modeling for healthcare innovation, quality assessment of educational resources, and decentralized identity systems. Recent publications highlight innovations in quantum software measurement, KlimaDAO analysis, and ethical AI traceability. He has also explored the impact of political events on social networks and hospital resource management through predictive analytics. Key contributions include frameworks for metadata quality in digital repositories, interlinking educational data with the Web of Data, and applying blockchain for secure metadata deployment. His research often intersects interdisciplinary domains, emphasizing social data visualization and user-centric repository design.

• Computer Science
• Software Engineering
• Data Management
• +7 more

Miguel Ángel Sicilia Urbán is a Full Professor in the Department of Computer Science at Universidad de Alcalá, Spain. His research focuses on software engineering, data management, and fuzzy systems, with recent work extending into blockchain technology, cryptocurrency, and semantic web applications. He holds a Ph.D. from Universidad Carlos III de Madrid (2003), where his thesis explored adaptive hypermedia models with imperfect information support. Research Interests His expertise spans collaborative filtering algorithms, software cost estimation, and the application of fuzzy logic in database systems. Current projects include metadata traceability, privacy-preserving computing, and the analysis of cryptocurrency markets. Sicilia is also active in semantic web technologies, including linked data integration and knowledge graph development. Publications & Collaborations With over 20 years of research output, Sicilia has authored/co-authored 11 indexed publications since 2001, including foundational work on OWA-based collaborative filtering and Choquet integral aggregation. Notable collaborations include projects with researchers like Elena García on usability criteria modeling and Juan J. Cuadrado-Gallego on software estimation models. Awards & Recognition While no specific awards were explicitly mentioned, his contributions to software engineering and data management have been recognized through multiple co-edited conference proceedings and citations in interdisciplinary fields like medical informatics and environmental economics. Contact Email: msicilia@uah.es

• Signal Processing
• Acoustic Sensor Networks
• Machine Learning Applications
• +5 more

Manuel Utrilla Manso is an Associate Professor at the Department of Signal Theory and Communications at the University of Alcalá. His research focuses on signal processing, acoustic sensor networks, and machine learning applications in audio-based systems. He leads the AES3 research group, specializing in smart acoustic and electromagnetic sensor networks and signal processing. His work addresses challenges in UAV acoustic detection, emotion recognition (e.g., anger detection), and speech processing for hearing aids. He holds a Ph.D. from the University of Alcalá with a thesis on finite-precision analysis in advanced digital filter structures. Key research interests include: Acoustic localization and source separation Machine learning for audio classification Optimization of microphone array configurations Non-destructive spectral analysis using sound Hearing aid algorithms and noise suppression Publications emphasize robust systems for noisy environments, computational efficiency, and interdisciplinary applications in healthcare and environmental monitoring. His work bridges theoretical signal processing with practical implementations in automotive, biomedical, and smart sensor domains. Notable contributions include development of benchmark datasets for sound event classification and optimization frameworks for waveform design in non-destructive testing. Research extends to educational technologies, including formative assessment methodologies for engineering students and interdisciplinarity in technical education.

• Computer Vision
• Artificial Intelligence
• Shape Recognition
• +7 more

José Javier Valero Más is a faculty member at the University of Alicante, affiliated with the Department of Software and Computing Systems within the School of Engineering. He holds the academic rank of Assistant Professor (Profesor Ayudante Doctor) and is actively engaged in teaching and research. His institutional affiliations include the Institute of Informatics Research (I.U. Investigación Informática) and the Shape Recognition and Artificial Intelligence research group. Academic Background: PhD in Computer Science, University of Alicante (2017) Máster in Sound and Music Computing, Universitat Pompeu Fabra (2013) M.Sc. in Telecommunications Engineering, Cork Institute of Technology (2012) Ingeniero en Telecomunicación, Universidad Miguel Hernández de Elche (2012) Ingeniero Técnico en Telecomunicación (Sonido e Imagen), University of Alicante (2009) Máster in Teacher Training, Universidad de Murcia (2020) Research Interests: José Javier Valero Más specializes in computer vision and artificial intelligence, with a particular focus on shape recognition. His work extends into sound and music computing, digital sound synthesis, and multimedia systems. His interdisciplinary background bridges engineering, computer science, and digital arts, enabling innovative approaches in both visual and audio domains. His research integrates programming, data structures, and signal processing techniques to solve complex problems in recognition and synthesis. Scientific Awards: No awards were mentioned in the provided text. Advising and Grants: There is no explicit information regarding graduate student advising, research grants, or funded projects in the provided materials. However, his affiliation with active research groups suggests participation in collaborative research initiatives. Labs and Teams: José Javier Valero Más is a member of the Shape Recognition and Artificial Intelligence research group at the University of Alicante. This group operates under the Institute of Informatics Research (I.U. Investigación Informática), focusing on pattern recognition, machine learning applications in visual data, and intelligent systems. The group fosters interdisciplinary research and contributes to both academic and applied technological advancements.

• Future of Work
• Sustainability of Human Resource Management
• Downsizing
• +6 more

Mónica Santana is a Full Professor at the Business Organization and Marketing Department of the Faculty of Business Sciences , Universidad Pablo de Olavide. Her academic career spans roles from Associate Professor (2009-2017) to Accredited University Professor (2024). She teaches subjects like Strategic Management of Human Resources, Business Economics, and International Human Resources Management across multiple programs including bachelor’s, double degrees, and master’s in International Business Management. Research interests include the Future of Work, Sustainability of HRM, Downsizing, and Bibliometric Analysis (SciMAT). Her work explores leadership, AI, digital skills, and employee well-being during crises, with a focus on organizational adaptation and social cohesion. Recent publications analyze topics such as coopetition in strategic management, AI-era competencies, remote work trends, and sustainable career paths for cancer survivors. These studies employ science mapping techniques to uncover patterns in global HRM, corporate governance, and post-pandemic workforce strategies. Scientific awards include the Extraordinary Doctorate Award and Mention of Teaching Excellence. Her thesis on HR strategies during crises earned Cum Laude and International Mention, with research at Ghent University. Professional experience spans roles as a Market Specialist at The Bank of New York (Belgium), a 10-year advisory position in Seville’s Autonomous Public Administration, and a grant-funded R&D project on digital transformation and HR sustainability (2022/00249/001). Collaborations include Ramón Valle-Cabrera and Barbara Larrañeta, with co-edited works like New Directions in the Future of Work (2021).

• Financial Economics
• Accounting
• Corporate Taxation
• +3 more

Dr. Julian Martinez Vargas is a Professor in the Department of Accountancy at the Faculty of Economics , Universitat de València. His research focuses on Financial Economics and Accounting , with particular emphasis on corporate income tax accounting, business failure prediction, and cooperative enterprise analysis. PhD in Accountancy from Universitat de València (2005) Specializes in empirical analysis of tax effects in financial statements Develops predictive models using machine learning (XGBoost, GBM) Explores fiscal pressure in cooperative enterprises across regions and industries Recent research trends include: Applying advanced algorithms to financial risk assessment Comparative studies of international stock market indices Analyzing tax treatment of subsidies and donations Investigating financial dynamics in post-conflict regions

Pilar Soriano FelipeView profile

Associate Professor

• Financial Economics
• Volatility Transmission
• Risk Management
• +7 more

Pilar Soriano Felipe is an Associate Professor in the Department of Financial and Actuarial Economics at the Faculty of Economics, Universitat de València, Spain. She is an active researcher affiliated with the EFEN (Financial Economics and Energy Finance) research group and has served in academic leadership as vice-dean of her faculty. Her educational background includes: Licenciatura in Business Management and Administration, Universitat de València BA (Hons) in European Management, University of Westminster, United Kingdom PhD in Quantitative Finance, Universitat de València (2007) Her research focuses on financial market dynamics, particularly volatility transmission , risk management , energy markets , green and Islamic finance , and financial ethics . She has been a visiting researcher at Tilburg University (Netherlands) and Universitat Pompeu Fabra (Spain), contributing to an international research profile. Her recent publications (2018–2024) show a strong trend toward integrating machine learning and statistical learning in forecasting equity risk premiums and analyzing macro-financial linkages, while continuing work on sustainable finance, energy markets, and financial stability. Topics include green bonds, environmental and Islamic mutual funds, and the impact of monetary policy on oil prices. Her scientific contributions have been published in reputable journals such as Financial Analysts Journal , Quantitative Finance , Journal of Business Ethics , Sustainability , and Journal of Cleaner Production . She has participated in multiple competitively funded research projects at regional and national levels. While no formal list of students is provided, her role as a PhD supervisor and senior faculty member suggests advising activity. She has also contributed to university governance, notably as vice-dean of the Faculty of Economics. She is actively involved in research through the EFEN group, which focuses on financial economics and energy finance, indicating ongoing collaboration and team-based research in these domains.

Vicent Girbés JuanView profile

Associate Professor

• Robotics
• Human-Robot Interaction
• Intelligent Vehicles
• +12 more

Vicent Girbés Juan is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is centered in the HRI Human-Robot Interaction Group, where he contributes to advanced robotics, intelligent vehicles, and human-centered automation systems. He earned his PhD from Universitat Politècnica de València in 2016 with a thesis on clothoid-based planning and control in autonomous and manual-assisted driving systems, supervised by Dr. Josep Tornero Montserrat and Dr. Leopoldo Armesto Ángel. His research interests span robotics, control systems, path planning for UAVs, visible light communication, haptic feedback in teleoperation, and educational innovation in engineering. He has published extensively on smooth trajectory generation, dual-arm robot control, sensor fusion, and V2V communications. His recent work shows a growing emphasis on integrating pedagogical innovation with engineering education, including flipped evaluation, peer assessment, and hackathon-based programming learning. His publications from 2021 to 2024 reveal a dual focus: advancing industrial robotics and intelligent transportation systems, while simultaneously innovating in teaching methodologies and student engagement in higher education. Key technical areas include clothoid-based 3D path planning, cautious Bayesian optimization, VLC positioning, and haptic-assisted teleoperation. He actively collaborates on interdisciplinary projects involving human-robot cooperation, industrial automation, and educational technology, reflecting a commitment to both technological advancement and pedagogical excellence. His email is vicent.girbes@uv.es .

• Nuclear Instrumentation
• Radiation-Hard Sensors
• Monolithic Active Pixel Sensors
• +7 more

Vicente González Millán is a full Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. His work bridges electronics technology and nuclear physics, with strong involvement in international collaborations such as CERN, AGATA, and NEDA. His research focuses on the development of advanced radiation-hard semiconductor sensors, particularly the MALTA series of monolithic active pixel sensors, for applications in high-energy and nuclear physics experiments. He is actively involved in detector design, signal processing, FPGA-based data acquisition systems, and real-time data analysis techniques. His work supports cutting-edge experiments in nuclear structure, particle detection, and medical physics instrumentation. The recent publications show a consistent focus on detector development, sensor characterization, and data acquisition systems. Key themes include radiation hardness, timing performance, charge calibration, and the use of machine learning for event reconstruction in neutron detection arrays. The work is highly experimental and closely tied to large-scale facilities like CERN and GANIL. He leads or is a core member of two research groups: i2N (Electronic Instrumentation in Medical and Nuclear Physics) and IRIMED (Research in Radiophysics and Nuclear Instrumentation in Medicine), highlighting the dual application of his work in both fundamental physics and medical technology. While no specific awards are listed, his sustained publication record and leadership in major detector projects indicate significant recognition in the field. He also contributes to the development of real-time trigger systems, digital front-end electronics, and FPGA-based solutions for gamma and neutron spectroscopy.

• Image Processing
• Computational Neuroscience
• Statistical Modeling
• +5 more

Valero Laparra Perez-Muelas is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is affiliated with the Image Processing Laboratory (ERI IPL) and the Image and Signal Processing (ISP) research group, focusing on interdisciplinary research at the intersection of electronics, signal processing, and neuroscience. His research interests include image processing, computational neuroscience, statistical modeling, and machine learning, particularly in understanding efficient image representations through the lens of biological and statistical principles. His work bridges engineering and neuroscience to develop biologically inspired models for visual processing. He earned his PhD from the University of Valencia in 2011 with a thesis titled Learning efficient image representations: connections between statistics and neuroscience , supervised by Dr. Jesús Malo and Dr. Gustavo Camps Valls. Although no publications or students are listed in the provided text, his research group affiliation suggests active supervision and collaboration. The available information does not mention any scientific awards or recent articles. He is actively engaged in research through the ISP group and the ERI Image Processing Laboratory, contributing to advanced signal and image analysis methodologies with applications in neuroscience and artificial intelligence.

• Electronics
• Biomedical Engineering
• Computational Physiology
• +5 more

ALEJANDRO LIBEROS MASCARELL is a faculty member at the School of Engineering, Universitat de València, where he is affiliated with the Department of Electronic Engineering. His research is centered within the COMMLAB (Computational Multiscale Simulation and Machine Learning Lab), focusing on advanced computational techniques applied to biomedical challenges, particularly in cardiac rhythm disorders. His research interests lie at the intersection of electronics, computational modeling, and medical diagnostics. Specifically, he works on mathematical modeling of reentrant atrial tachyarrhythmias , leveraging multiscale simulations and machine learning to improve diagnosis and treatment. His work bridges engineering and clinical cardiology, aiming to translate computational insights into practical clinical tools. The research trends reflected in his background indicate a strong focus on biomedical signal processing , cardiac simulation , and AI-driven medical modeling , particularly in the context of arrhythmia mechanisms. These efforts are supported by interdisciplinary collaboration and integration within a specialized computational lab environment. Scientific Awards: No scientific awards mentioned in the provided text. Regarding advising and grants, there is no explicit information available on student supervision or funded research projects. However, his doctoral background and lab affiliation suggest potential involvement in research training and collaborative scientific initiatives. He is actively involved with the COMMLAB – Computational Multiscale Simulation and Machine Learning Lab , a research group dedicated to developing and applying advanced computational methods for solving complex problems in science and engineering, particularly in biomedical domains.

• Internet of Things (IoT)
• Edge Computing
• Deep Learning
• +12 more

Jesus Lopez Ballester is a faculty member at the Universitat de València, affiliated with the School of Engineering and the Department of Electronic Engineering. He is an active researcher in the fields of IoT, edge computing, and acoustic signal processing, contributing to both academic and applied research in smart environments and sustainable monitoring systems. His research interests lie at the intersection of artificial intelligence and physical sensing systems, with a strong emphasis on real-time, low-cost, and energy-efficient solutions. Key areas include sound event detection for driving safety, psychoacoustic annoyance modeling, urban soundscape monitoring, HRTF analysis, and room acoustic parameter estimation. His work integrates deep learning with embedded systems, particularly leveraging edge computing and IoT platforms to enable intelligent, distributed acoustic sensing in urban and industrial contexts. The recent publications reveal a consistent trend toward deploying AI models on resource-constrained devices for environmental and safety applications. His work frequently combines sensor networks with neural networks to achieve real-time, on-device inference, supporting applications in smart cities, transportation safety, and industrial monitoring. There is a strong focus on sustainability, cost-effectiveness, and practical deployment over theoretical exploration. He is actively involved in two research groups: the DSDC Communications and Digital Systems Design Group and the LSyM Simulation and Modeling Laboratory, where he contributes to projects involving wireless sensor networks, acoustic modeling, and IoT system design. While no formal students are listed, his role in supervising thesis work and leading technical developments indicates mentoring and advisory responsibilities. His research is supported by institutional affiliations and collaborative projects, though specific grants are not detailed in the provided text.

• Statistics
• Probability Theory
• Stochastic Processes
• +5 more

Guillermo Ayala Gallego is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. His research is centered on statistical and probabilistic modeling with applications in image analysis and biological systems. Research Interests: His work spans Statistics , Probability Theory , Stochastic Processes , Image Analysis , Mathematical Morphology , and Spatial Statistics . He applies these to areas such as pattern recognition, biomedical imaging, and content-based image retrieval. Publication Trends: His publications, spanning from 1991 to 2011, show a consistent focus on the intersection of statistical theory and image processing. Key themes include the use of Boolean models , spatial point pattern analysis , mathematical morphology , and Bayesian frameworks for image retrieval and classification. His work often involves the analysis of biological and medical images, particularly the human corneal endothelium. Education: PhD in Statistics, University of Valencia, 1988 Thesis: "Inferencia en modelos booleanos" (Supervised by Dr. Francisco Montes Suay) Collaborations and Affiliations: He is a member of the IARM (Image Analysis, Modelling and Retrieval) research group. He has collaborated extensively with researchers such as Amelia Simó, Esther de Ves, Juan Domingo, and Miguel López-Díaz. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Information on students, advisees, grants, or projects is not available in the provided text.

• Physiotherapy
• Machine Learning
• Data Analysis
• +3 more

Juan Jose Carrasco Fernandez is a Doctoral Researcher at the Faculty of Physiotherapy, Universitat de València (UV), Spain, where he contributes to advanced research in physiotherapy and data science. He is actively involved in two prominent research groups: IDAL (Intelligent Data Analysis Laboratory) and PTinMOTION (Physiotherapy in Motion. Multispeciality Research Group), reflecting his interdisciplinary expertise. His research focuses on the integration of machine learning and intelligent data analysis in physiotherapy and environmental modeling. Key areas include predictive modeling of climate variables , visualization techniques , and data-driven rehabilitation systems . His work bridges computational methods with clinical applications, promoting innovation in multispecialty physiotherapy. He completed his doctoral studies at the Universitat de València in 2020 with a thesis titled "Visualización, predicción y análisis de variables climáticas del océano atlántico mediante técnicas de aprendizaje automático" (Visualization, prediction, and analysis of Atlantic Ocean climate variables using machine learning techniques), supervised by Dr. Joan Vila Francés, Dr. Antonio Geraldo Ferreira, and Dr. Juan Gómez Sanchís. Juan Jose Carrasco Fernandez has not yet published articles listed in the provided data, but his research trajectory suggests strong contributions in data-intensive health sciences. He has not received any scientific awards mentioned in the source text. He is engaged in collaborative research within structured teams rather than individual student supervision. His work is embedded within institutional research groups, emphasizing team-based inquiry and cross-disciplinary projects. He is a core member of the IDAL - Intelligent Data Analysis Laboratory and the PTinMOTION - Physiotherapy in Motion. Multispeciality Research Group , where he applies data science to clinical and environmental challenges.