Find Academic Supervisors

• Fluid Mechanics
• Computational Fluid Dynamics
• Turbulence Modeling
• +4 more

Juan Grau Barcelo is a Professor in the Department of Fluid Mechanics at the Escola d'Enginyeria de Barcelona Est (EEBE) of the Universitat Politécnica de Catalunya (UPC). He is actively involved in multiple research groups including GReCEF (Research Group in Fluid Science and Engineering) and CER-H2 (UPC Hydrogen Research Center). His research focuses on fluid mechanics, computational fluid dynamics, turbulence modeling, and environmental fluid dynamics with particular emphasis on microplastics in ocean environments. His work spans both fundamental fluid dynamics research and practical engineering applications, including hydrogen technology and computational methods for fluid flow simulation. Analysis of his recent publications shows a strong trend toward interdisciplinary research combining computational fluid dynamics with environmental science, particularly in the area of microplastic transport and aggregation in ocean turbulence. His work increasingly incorporates machine learning techniques for data reconstruction and virtual interaction with engineering systems, showing adaptation to emerging computational methodologies. His research has been supported by various competitive R&D projects including European Horizon 2020 initiatives and Spanish National Research Programs focused on ecological transition and digital transformation. Grau Barcelo has demonstrated significant contributions to both research and teaching innovation, with recent projects focused on active learning methods in thermofluid mechanics education through flipped lectures and problem-based learning activities. He maintains active collaborations with researchers across multiple institutions, with notable long-term collaborations with Ricardo Torres Camara, Lluís Jofre Cruanyes, and Francesco Capuano, among others.

Jesús Ureña UreñaView profile

Professor

• Electronic Engineering
• Positioning Systems
• Infrared Sensors
• +3 more

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).

• Constraint Satisfaction Problems
• Computational Complexity
• Theoretical Computer Science
• +4 more

Victor Dalmau is an Associate Professor at the Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF). He holds a Ph.D. and a degree in Computer Science from Universitat Politècnica de Catalunya. His career includes post-doctoral roles at institutions such as the University of Zaragoza and University of California, Santa Cruz, before joining UPF in 2000. He has held visiting positions at prestigious universities including Oxford, Cambridge, and Simon Fraser University. His research focuses on theoretical computer science, particularly constraint satisfaction problems (CSPs), exploring their complexity, combinatorial structures, and applications in areas like artificial intelligence and database theory. His work integrates concepts from universal algebra, logic, and computational learning theory. Notably, he investigates algorithmic techniques like Sherali-Adams hierarchies and Weisfeiler-Leman invariance for solving CSPs efficiently. Recent publications emphasize advancements in constraint satisfaction frameworks, homomorphism dualities, and the interplay between logic and database query optimization. His research bridges foundational theory with practical challenges in distributed computing and algorithm design. Victor Dalmau’s contributions span over 50 peer-reviewed articles, with a focus on computational complexity classifications, constraint programming, and the theoretical underpinnings of database systems. His work often addresses the boundaries between tractability and intractability in combinatorial problems.

• Operations Research
• Algorithms and Data Structures
• Healthcare Optimization
• +3 more

Jesica De Armas Adrián is an Associate Professor at Universitat Pompeu Fabra's Barcelona School of Management, specifically in the Department of Operations, Technology & Science. She holds a Doctorate in Computer Science from the University de La Laguna (Spain) and the University of Edinburgh (UK). Her academic career includes teaching roles at the University of La Laguna and the Universitat Oberta de Catalunya. Her research focuses on quantitative methods for business optimization, distribution systems, algorithms, and combinatorial optimization applied to healthcare, urban planning, and logistics. Key areas include optimizing home care services, public school accessibility in developing countries, and emergency response systems for roadside incidents. Her work spans over 15 publications from 2019–2024, emphasizing heuristic algorithms, stochastic modeling, and multi-objective optimization in real-world operations challenges. Notable contributions address aging population needs, pandemic impact assessments, and sustainable infrastructure solutions. Professional activities include participation in major conferences such as EURO 19 Dublin and Verolog, focusing on integrated healthcare systems and urban policy applications. She actively contributes to bridging theoretical optimization research with practical societal challenges.

Noelia Hernández ParraView profile

Associate Professor

• Systems Engineering
• Automation
• Intelligent Vehicles
• +3 more

Noelia Hernández Parra is an Associate Professor at the University of Alcalá, affiliated with the Department of Automática. Her research focuses on intelligent vehicles, traffic technologies, and WiFi-based localization systems. She holds a PhD in Systems Engineering and Automation from the same university (2014), with a thesis titled Smart hierarchical WiFi localization system for indoors , supervised by Dr. Manuel Ocaña Miguel and Dr. José M. Alonso Moral. Her work integrates systems engineering, wireless communication, and machine learning to address challenges in autonomous systems, urban navigation, and customer activity analysis in retail environments. Key contributions include developing CNN-based localization systems (WiFiNet), vehicle trajectory prediction models, and fusion techniques for sensor data in autonomous vehicles. Research interests span WiFi localization, deep learning architectures for spatial analysis, and intelligent traffic management. She leads the INVETT research group (Intelligent Vehicles and Traffic Technologies), focusing on vehicular autonomy and smart infrastructure integration. Her recent publications emphasize urban localization, sensor fusion, and autonomous driving systems. No awards are explicitly mentioned in the provided text. Advising and grants details are not elaborated, though her PhD supervision and research group indicate active mentorship and potential grant involvement. She collaborates on projects combining localization technologies with automotive and retail applications.

Daniel Rodríguez GarcíaView profile

Associate Professor

• Data Mining
• Software Engineering
• Machine Learning
• +3 more

Daniel Rodríguez García is an Associate Professor (tenured) at the Department of Computer Science, University of Alcalá, Madrid, Spain. He also serves as a visiting researcher at Oxford Brookes University, UK, and previously held roles as an online tutor at the Universitat Oberta de Catalunya (UOC) and lecturer at the University of Reading (2001–2010). His academic credentials include a Computer Science degree from the University of the Basque Country and a PhD from the University of Reading (2003), complemented by a Postgraduate Certificate in Academic Practice (2005). His research focuses on data mining and software engineering , particularly applying machine learning techniques to software engineering challenges like defect prediction, classifier optimization, and cybersecurity. He leads the PROGRESSUS (Programming & Sustainability) and TIFyC (Information Technologies for Training & Knowledge) research groups, emphasizing sustainable software practices and educational technology innovation. His work spans topics such as DevOps monitoring, inclusive e-learning platforms, and AI-driven threat analysis in manufacturing industries. Notable contributions include the POEMA personal cloud system for education and studies on ransomware patterns in the Dark Web. His articles reflect trends in multi-objective optimization, imbalanced data handling, and ethical AI integration. He actively participates in workshops like RAISE (Realizing AI Synergies in Software Engineering), fostering cross-disciplinary collaboration.

• Operations Research
• Mathematical Programming
• Combinatorial Optimization
• +7 more

Jose Manuel Tamarit Goerlich is an Associate Professor in the Department of Statistics and Operations Research at the Universitat de València. He has been actively contributing to the field of operations research since the late 1980s, with a strong focus on combinatorial optimization and heuristic methods for scheduling and packing problems. His research interests include Operations Research , Mathematical Programming , Combinatorial Optimization , Heuristic Algorithms , Project Scheduling , and Bin Packing . His work often involves developing exact and metaheuristic algorithms for complex resource-constrained and logistics problems. The recent publications reflect a consistent trend in cutting and packing problems , project scheduling , and metaheuristic development such as GRASP, VNS, and path relinking. These works span applications in container loading, strip packing, and berth allocation, demonstrating both theoretical depth and practical relevance in industrial and logistical settings. He has collaborated extensively with researchers such as Ramon Alvarez-Valdes and Francisco Parreño, leading to numerous joint publications in leading journals like European Journal of Operational Research and Computers & Operations Research . No scientific awards or student advisees are listed in the available data. He has advised on or conducted research involving algorithm design for timetabling, pallet loading, and job-shop scheduling, particularly in industrial contexts such as glass manufacturing and airport operations.

• Program Analysis
• Software Verification
• Debugging
• +13 more

Manuel V. Hermenegildo is a Distinguished Professor at the IMDEA Software Institute and a Full Professor in the Department of Artificial Intelligence at the Technical University of Madrid (UPM). He is the founding Scientific Director of IMDEA Software and leads the CLIP Lab, a joint research unit between UPM and IMDEA Software focused on computational logic, programming languages, and parallelism. His career spans leadership in national research policy, academic positions in Spain and the U.S., and extensive international collaboration. PhD in Electrical and Computer Engineering, University of Texas at Austin (1986) M.S. in Electrical and Computer Engineering, University of Texas at Austin (1984) M.S. in Electrical Engineering, Technical University of Madrid (1981) His research centers on global program analysis, verification, debugging, and optimization for functional and non-functional properties such as time, memory, and energy. He has pioneered work in abstract interpretation, partial evaluation, constraint and logic programming, parallelizing compilers, and computational logic. His methodologies support the development of safe, efficient, and reliable software systems. The integration of static and dynamic analysis, resource-aware compilation, and assertion-based debugging are recurring themes in his work. His recent publications emphasize incremental and modular analysis, assertion checking, energy-aware computing, and verification via Horn clauses. Trends show a strong focus on making static analysis more practical, scalable, and integrated into real-world development workflows, particularly for logic and declarative languages. He frequently publishes in top venues such as TPLP, SCP, TOPLAS, and POPL. ACM Fellow Julio Rey Pastor Prize (2006) Aritmel Prize (2005) Elected member, Academia Europaea (2010) Test of Time Award, ICLP 2017 Best Paper Awards at ICSOC 2011 and ICLP 2005 Fulbright-Hays Scholarship Hermenegildo has supervised numerous PhD students and postdocs, many of whom are now leading researchers. He has been principal investigator in over 50 research projects funded by the EU, Spanish government, NSF, and industry partners. His leadership extends to national research policy, where he directed Spain’s National Research Directorate and launched the Ramón y Cajal fellowship program. He has served on tenure and evaluation committees for institutions worldwide and is active in shaping research agendas through roles in Informatics Europe and EIT Digital. He leads the CLIP Lab, which develops advanced program analysis tools and systems such as the Ciao programming environment, CiaoPP static analyzer, and LPdoc documentation generator. The lab fosters collaboration across academia and industry, focusing on next-generation program development tools, verification, and resource-aware computing.

• Machine Learning
• Computational Intelligence
• Data Mining
• +5 more

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.

• High Performance Computing
• Parallel Computing
• GPU Programming
• +5 more

Margarita Amor López is a Professor in the Department of Computer Engineering at the Faculty of Computer Science, Universidade da Coruña. She specializes in High Performance Computing and Computer Architecture, leading the Computer Architecture Group research team. Her teaching spans multiple degree programs including Computer Engineering, Data Science and Engineering, Artificial Intelligence, and various Master's programs. Her research interests focus on GPU programming, parallel computing, heterogeneous architectures, and their applications in LiDAR processing, extended reality, and digital cultural heritage. She has developed numerous techniques for optimizing parallel codes on GPUs and heterogeneous systems, with applications in geospatial data processing and computer vision. Her publications demonstrate a strong focus on improving performance of parallel algorithms on modern architectures, particularly GPUs. She has made significant contributions to memory management techniques, optimization of parallel algorithms, and application of these techniques to real-world problems like power line detection in LiDAR data and extended reality applications. She has supervised numerous Master's and PhD theses, mentoring students in areas of high performance computing and computer architecture. Her research has been funded by multiple national and European projects from the Ministry of Science and Innovation, Xunta de Galicia, and the European Union. Dr. Amor López has also registered multiple software developments related to her research, demonstrating the practical applications of her work in high performance computing and parallel systems.

• Big Data
• Time Series Forecasting
• Machine Learning
• +5 more

Ruben Pérez Chacón is an Associate Professor at Pablo de Olavide University , affiliated with the Department of Sports and Computer Science . His research focuses on Big Data , Time Series Forecasting , and Machine Learning , particularly in energy consumption and optimization algorithms. Contact: rpercha@upo.es . PhD in Engineering and Data Science (2021), Pablo de Olavide University. Research Interests include developing pattern-based algorithms for multivariate big data forecasting, energy analytics in smart cities, and bioinspired optimization techniques like the Coronavirus Optimization Algorithm. His work bridges Computer Science with practical applications in Energy Systems and Sustainable Urban Planning . Publications highlight advancements in Distributed Computing (Spark), Metaheuristic Algorithms , and Electricity Demand Prediction , reflecting a strong emphasis on Applied Artificial Intelligence and Data Mining .

• Machine Learning
• Deep Learning
• Computer Science
• +12 more

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.

FRANCISCO SANTIAGO ARGUELLO PEDREIRAView profile

Professor

• Remote Sensing
• Multispectral Imaging
• Hyperspectral Imaging
• +5 more

Francisco Santiago Argüello Pedreira is a Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing within the Higher Technical School of Engineering. He holds a PhD from the same institution (1992) with a thesis on parallel-segmented architectures for orthogonal transforms, supervised by Dr. Emilio López Zapata. His research focuses on remote sensing, multispectral/hyperspectral imaging, machine learning, and GPU programming. He leads the ARQCOMP (Computer Architecture) research group and has pioneered GPU-accelerated methods for image registration, anomaly detection, and classification. Key contributions include hybrid parallel computing frameworks (CUDA/OpenMP/MPI), GAN-based domain adaptation, and innovative data augmentation techniques for hyperspectral imagery. His work spans environmental applications (e.g., Galician river monitoring), forestry mapping, and high-resolution land cover analysis. Notable projects include the EffBaGAN and ResBaGAN models for addressing data scarcity and forest mapping challenges. He has developed open-source tools like HypeRvieW for hyperspectral data processing. Publications emphasize algorithm optimization for GPU architectures, with a focus on real-time processing and heterogeneous computing platforms. His research bridges computer architecture advancements with practical applications in Earth observation and environmental science.

JOSE CARLOS CABALEIRO DOMINGUEZView profile

Professor

• High Performance Computing
• Parallel Systems Architecture
• Parallel Algorithms
• +7 more

Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.

• High-performance computing
• Decimal arithmetic
• Parallel programming
• +4 more

Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.